<jupyter_start><jupyter_text>In this notebook, we need to show the case for applying both identification and estimation.<jupyter_code>import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath('./introduction.ipynb'))))
from ylearn.estimator_model.base_models import BaseEstLearner<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10385756676557864, 'fraction_numerical': 0.0, 'mean_word_length': 8.135135135135135, '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': '233583', 'license': 'Apache-2.0', 'n_tokens_mistral': 112, 'n_tokens_neox': 109, 'n_words': 23, 'path': 'example_usages/introduction.ipynb', 'repo_name': 'DataCanvasIO/YLearn', 'size': '337'}