lab 5

 import gensim.downloader as api

model = api.load("glove-wiki-gigaword-50")
def make_paragraph(seed):
    sims = model.most_similar(seed, topn=5)
    s = [w for w, _ in sims]
    para = (f"In the spirit of {seed}, one may explore {s[0]}, {s[1]} and {s[2]}. "
            f"Through {s[3]} and {s[4]}, every journey becomes memorable.")
    return s, para
seed_word = "adventure"
similar_words, paragraph = make_paragraph(seed_word)
print("Similar words:", similar_words)
print("\nParagraph:\n", paragraph)

Comments

Post a Comment

Popular posts from this blog

lab 6

  from transformers import pipeline sentiment = pipeline( "sentiment-analysis" ) sentences = [   "I love this product! It works perfectly." ,   "This is the worst experience I've ever had." ,   "The weather is nice today." ] results = sentiment(sentences) for s, r in zip (sentences, results):     print ( f " {s} -> {r[ 'label' ]} ( {r[ 'score' ] :.2f } )" )
Read more

lab 1

  ! pip install gensim nltk transformers torch matplotlib scikit-learn import gensim.downloader as api model = api.load( "glove-wiki-gigaword-50" ) w1, w2, w3 = "king" , "man" , "woman" vec = model[w1] - model[w2] + model[w3] word = model.most_similar([vec], topn= 1 )[ 0 ][ 0 ] print ( f " {w1} - {w2} + {w3} ≈ {word} " )
Read more