<jupyter_start><jupyter_text>Data preprocessing<jupyter_code>datas = pd.read_csv('batch_size.csv', delimiter=',',
            names=['model_id', 'task_id', 'batch_id', 'hops', 'nb_epoch',
                   'epoch', 'epoch_date', 'epoch_timestamp', 
                   'train_size_prct', 'train_size', 'val_size', 'test_size',
                   'train_acc', 'val_acc', 'test_acc'])
df = datas[['model_id', 'epoch', 'test_acc']].copy()
df = pd.pivot_table(df, values='test_acc', columns=['model_id'], index='epoch')
df = datas[['model_id', 'epoch', 'test_acc']].copy()
df = pd.pivot_table(df, values='test_acc', columns=['model_id'], index='epoch')
_ = df.plot(title='test accuracy over training')
model = 'n'
a = df[model].plot(title='test accuracy over training')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19748427672955976, 'fraction_numerical': 0.0, 'mean_word_length': 4.896296296296296, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '166876', 'license': 'MIT', 'n_tokens_mistral': 286, 'n_tokens_neox': 285, 'n_words': 48, 'path': 'output/.ipynb_checkpoints/notebook-checkpoint.ipynb', 'repo_name': 'CharlieBrugvin/chatbot-memN2N-tensorflow-analysis', 'size': '795'}