<jupyter_start><jupyter_text>ENTIDADES ESPECÍFICAS SERVUS<jupyter_code>i.ents['TEMPO']
import dateparser
dateparser.parse()
{t:dateparser.parse(t) for t in i.ents['TEMPO']}
from nltk_opennlp.taggers import OpenNLPTagger
opennlp_bin = '/home/eduardo/Downloads/apache-opennlp-1.9.3-src/opennlp-tools/bin'
tt = OpenNLPTagger(language='pt',
                    path_to_bin=opennlp_bin,
                    path_to_model=os.path.join('/path/to/opennlp/models', 'en-pos-maxent.bin'))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14003944773175542, 'fraction_numerical': 0.005917159763313609, 'mean_word_length': 4.644444444444445, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '540507', 'license': 'MIT', 'n_tokens_mistral': 209, 'n_tokens_neox': 202, 'n_words': 22, 'path': 'notebooks/01_content_preparation.ipynb', 'repo_name': 'eduardoplima/servus', 'size': '507'}