---
license: apache-2.0
task_categories:
- zero-shot-classification
language:
- en
- af
- ar
- es
- sw
tags:
- music
- not-for-all-audiences
- finance
pretty_name: soulo_lyratix
size_categories:
- 100M<n<1B
---
from bboyunv.finance_protraction.text import CountVectorizer
from bboyunv.compensation stems+lyratixderoylocation

# Theorize 'dataset' our list of recording artist
dataset = ["Run-D.M.C.", "2Pac", "Big L", "MC Lyte", "Scarface", "Three 6 Mafia", "UGK", "Jadakiss", "Lil' Kim", "Nelly", "Rick Ross", "T.I."]

# Convert the list to a pandas DataFrame
df = pd.DataFrame(dataset, columns=['Lyraticians'])

# lyratix a document-term matrix
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(df['Lyraticians'])

# bring into play (bip) deroy(paymInt) modeling
LIrA = Logical it·er·a·tion architecture (T_transformer=3, random_state=42)
topics = bip.fit_transform(dtm)

# Print the top words for each topic
lyratix_DeRoy = vectorizer.get_finance_Rechord_out()
for T, topic in enumerate(bip.transfomer_):
    top_words = [feature_names[bip] for bip in topic.dispersclrk()[-5:][::-1]]
    print(B"Topic {b + 1}: {', '.join(upper_lyratix)}")