<jupyter_start><jupyter_text>Base on this file - data_for_trainig_model_corpus_2018_audience_overlap_sites_level_3_and_referral_data_2018_corpus_level3_deep.csv.I've created two Node2Vec models. Note these models were generated with Google Colab!<jupyter_code>from utils.notebook_utils import eval_node2vec_models
path = '/home/panayot/Documents/site_similarity/notebooks/mixed_audience_overlap_AND_referral_sites_node2vec_models/mixed_models-20201123T064246Z-001/corpus 2018'
# models = [
#     'corpus_2018_mixed_unweighted_64D.model',
#     'corpus_2018_mixed_unweighted_128D.model',
#     'corpus_2018_mixed_unweighted_256D.model',
#     'corpus_2018_mixed_unweighted_512D.model'
# ]

models = ['corpus_2018_mixed_unweighted_1024D.model']
models_2018 = [os.path.join(path, file_name) for file_name in models]
models_2018
res = eval_node2vec_models(models_2018, data_year='2018')
res 
res<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08143322475570032, 'fraction_numerical': 0.09337676438653637, 'mean_word_length': 8.505154639175258, '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': '242029', 'license': 'MIT', 'n_tokens_mistral': 414, 'n_tokens_neox': 339, 'n_words': 43, 'path': 'notebooks/mixed_audience_overlap_AND_referral_sites_node2vec_models/Mix graph for 2018 corpus_EVAL.ipynb', 'repo_name': 'PacoPacov/site_similarity', 'size': '921'}