import re

pattern = 'awesome'

content = 'Duckduck go is awesome and it is getting better everyday'

match = re.search(pattern, content)

#print(match)

start = match.start()
end = match.end()

#print(match.string)

print(start, end)

15 22 …

import requests
from bs4 import BeautifulSoup

# Collect and parse first page
page = requests.get('https://www.springfieldspringfield.co.uk/view_episode_scripts.php?tv-show=two-and-a-half-men&episode=s01e01')
soup = BeautifulSoup(page.text, 'html.parser')  

content = soup.select('div.scrolling-script-container')[0 …

import tensorflow as tf

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

hello = tf.constant('Hello, TensorFlow!')

hello

<tf.Tensor 'Const:0' shape=() dtype=string>

sess = tf.Session()
sess.run(hello)

b'Hello, TensorFlow!'

a = tf.constant(20 …

import tensorflow as tf

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.constant(5.0)

a

<tf.Tensor 'Const:0' shape=() dtype=float32>

b = tf.constant(3.0)

b

<tf.Tensor 'Const_1:0' shape=() dtype=float32 …

print("This is simple test")

This is simple test

28

print("This is simple test 2")

This is simple test 2

import re
from nltk.tokenize import word_tokenize
from collections import Counter
from nltk.corpus import stopwords

text = ("""The cat is in the box. The cat likes the box. The box is over the cat.""")

tokens = [w for …

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml311; pyv: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0]'

print(pyu.ps2("transformers"))

transformers==4.47.0

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "Helsinki-NLP/opus-mt-en-fi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

2024-12 …

from sklearn import tree

X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

print(clf.predict([[21., 21.]]))

[1]

import numpy as np
import matplotlib.pylab as plt

x = np.arange(20, 29)

print(x)

[20 21 22 23 24 25 26 27 28]

y = np.sin(x)

print(y)

[ 0.91294525  0.83665564 -0 …