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README.md

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-# dockercomp-artifact
+# Readme
+This repository includes our data and code. We intend to enhance the explanation of this artifact and add additional data if the paper is accepted.
 
+## Environment Preparation
+
+CPU:  Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz with 12 core processors, and 32G RAM.
+GPU: NVIDIA RTX 3090 GPU with 24 GB memory
+Packages: 
+`transformers 4.34.1`
+`tokenizers 0.14.1`
+`torchmetrics 1.2.0`
+`torch 2.1.0`
+`scikit-learn 1.3.2`
+`GPUtil 1.4.0`
+`numpy 1.26.1`
+`evaluate 0.4.1`
+`numba 0.58.1`
+`nltk 3.8.1`
+`tqdm 4.66.1`
+`typing 4.8.0`
+`psycopg2 2.9.9`
+## Code Files
+`model.py`: code for model training. We implement our model with the popular deep learning development framework PyTorch and the python package transformers developed by HuggingFace. 
+`testing_tc.py` and `testing_lc.py`: code for model testing. We use two evaluation metrics for the TC task, namely the Accuracy (Acc) of the top prediction and the
+MRR for the top-5 recommendations. Five commonly used evaluation metrics are employed for the LC task: EM, ED, BLEU, ROUGE, and METEOR.
+`token_types.py`: code for analysis of model performance when predicts different token types. 
+`tokenizer.py`: code for tokenizer.  We use sub-word tokenization with the Byte-Pair Encoding (BPE) algorithm, as previous studies found that BPE can substantially reduce the vocabulary size
+and alleviate the OOV problem.
+`statistics.R`: We enhance the automatic evaluation by conducting statistical tests. We employ the McNemar’s test (with Odds Ratios) and the Wilcoxon signed-rank test (with Cliff’s delta) on the metrics. Holm’s correction procedure is employed to account for multiple comparisons in
+both statistical tests to adjust p-values.
+## Data Files
+The dataset contains 89,141 instances for training, 11,143 for validation, and 11,143 for testing. 
+`train_data_tc_task`: Training, validation, and testing data for the TC task.
+`train_data_lc_task`: Training, validation, and testing data for the LC task.
+
+`human_scores`: We consider three evaluation criteria: Similarity (s), Naturalness (n), and Informativeness (i). The score values range from 1 to 4, with a higher score indicating a higher quality of the predicted lines. Columns are suffixed with 1 and 2 to indicate Student 1 and Student 2, respectively.