<jupyter_start><jupyter_text>Intro spaCy relies on models that are language-specific and come in different sizes. You can load a spaCy model with spacy.load.Before using spacy dnld the language by:python -m spacy download enTo load english language<jupyter_code>import spacy
nlp = spacy.load('en')
type(nlp)
doc = nlp("Tea is healthy and calming, don't you think?")
print(doc)
type(doc)
for token in doc:
    print(token)
print(f"Token \t\tLemma \t\tStopword".format('Token', 'Lemma', 'Stopword'))
print("-"*40)
for token in doc:
    print(f"{str(token)}\t\t{token.lemma_}\t\t{token.is_stop}")
doc.similarity<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13166144200626959, 'fraction_numerical': 0.003134796238244514, 'mean_word_length': 5.726315789473684, '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': '89832', 'license': 'MIT', 'n_tokens_mistral': 241, 'n_tokens_neox': 228, 'n_words': 66, 'path': 'dependencyCodes/Studying/SpacyCoursePractice.ipynb', 'repo_name': 'puthusseri/MainProject', 'size': '638'}