<jupyter_start><jupyter_text>CRF<jupyter_code>from nltk.tag import CRFTagger
from nltk.corpus import treebank

train_data = treebank.tagged_sents()[:30]
test_data = treebank.tagged_sents()[3000:]

CRF = CRFTagger()
CRF.train(train_data,'crf_tagger_model')

'accuracy: ' + str(round(CRF.evaluate(test_data), 3))
CRF.tag(['the', 'men', 'attended', 'to', 'the', 'meetings'])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17206982543640897, 'fraction_numerical': 0.017456359102244388, 'mean_word_length': 9.307692307692308, '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': '409934', 'license': 'MIT', 'n_tokens_mistral': 178, 'n_tokens_neox': 165, 'n_words': 24, 'path': 's4/codes/crf.ipynb', 'repo_name': 'gebakx/ihlt', 'size': '401'}