<jupyter_start><jupyter_text>Split Labeled & Unlabeled Training Data<jupyter_code>train_data = data['train']

df_train = pd.DataFrame(columns=['label','query'])

for lst in train_data:
    df_train = df_train.append({'label':lst[1],'query':lst[0]},ignore_index=True)
df_train
#1st Variation
split_labeled(labeled_prop=10,unlabeled_prop=90)
#2nd Variation
split_labeled(labeled_prop=20,unlabeled_prop=80)
#3rd Variation
split_labeled(labeled_prop=40,unlabeled_prop=60)
#4th Variation
split_labeled(labeled_prop=60,unlabeled_prop=40)
#5th Variation
split_labeled(labeled_prop=80,unlabeled_prop=20)
#6th Variation
split_labeled(labeled_prop=90,unlabeled_prop=10)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12191582002902758, 'fraction_numerical': 0.04644412191582003, 'mean_word_length': 14.333333333333334, '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': '192501', 'license': 'Apache-2.0', 'n_tokens_mistral': 292, 'n_tokens_neox': 259, 'n_words': 33, 'path': 'data_CLINC150/Data_Preparation.ipynb', 'repo_name': 'louisowen6/GAN_BERT_CLINC150', 'size': '689'}