<jupyter_start><jupyter_text>Gender<jupyter_code>plt.rcParams['figure.figsize'] = [5, 5]
sns.countplot([x['gender'] for x in data])<jupyter_output><empty_output><jupyter_text>Professions<jupyter_code>plt.rcParams['figure.figsize'] = [15, 10]
plt.xticks(rotation=90)
sns.countplot([x['title'] for x in data])<jupyter_output><empty_output><jupyter_text>Professions & Gender<jupyter_code>plt.rcParams['figure.figsize'] = [15, 10]
plt.xticks(rotation=90)
sns.countplot(x=[x['title'] for x in data], hue=[x['gender'] for x in data])
(set([x['title'] for x in data]))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18781725888324874, 'fraction_numerical': 0.023688663282571912, 'mean_word_length': 10.84, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '365564', 'license': 'Apache-2.0', 'n_tokens_mistral': 258, 'n_tokens_neox': 240, 'n_words': 35, 'path': 'data_preparation_biosbias/data sanity.ipynb', 'repo_name': 'fprost/conversationai-models', 'size': '591'}