rm script

test.py

@@ -1,32 +0,0 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import pandas as pd
-import matplotlib.pyplot as plt
-
-df1 = pd.read_parquet('data/train-pairs.parquet')
-df2 = pd.read_parquet('data/test-pairs.parquet')
-
-tokenizer = AutoTokenizer.from_pretrained('bigcode/santacoder')
-
-df = pd.concat([df1, df2], axis=0)
-df['tokens'] = df['declarations'].apply(lambda x: tokenizer.tokenize(x))
-
-mean_tokens = df['tokens'].apply(lambda x: len(x)).mean()
-max_tokens = df['tokens'].apply(lambda x: len(x)).max()
-min_tokens = df['tokens'].apply(lambda x: len(x)).min()
-num_long_items_2048 = df[df['tokens'].apply(lambda x: len(x) > 2048)].shape[0]
-proportion_2048 = num_long_items_2048 / df.shape[0]
-num_long_items_256 = df[df['tokens'].apply(lambda x: len(x) > 256)].shape[0]
-proportion_256 = num_long_items_256 / df.shape[0]
-
-plt.hist(df['tokens'].apply(lambda x: len(x)), bins=25, range=(0, 2048))
-plt.xlim(0, 2048)
-plt.xlabel('Number of Tokens')
-plt.ylabel('Count')
-plt.title('Distribution of Type Declaration Num Tokens')
-
-# Add a label to the plot with the mean, max, min, and proportion
-label1 = f"Mean: {mean_tokens:.2f}\nMax: {max_tokens}\nMin: {min_tokens}\nProportion > 256: {proportion_256:.2f}\nProportion > 2048: {proportion_2048:.2f}"
-plt.gca().text(0.95, 0.95, label1, transform=plt.gca().transAxes, fontsize=14, verticalalignment='top', horizontalalignment='right')
-
-plt.savefig('declaration_token_distr.png')
-plt.show()