from gliner import GLiNER

# Initialize GLiNER with a pre-trained model
model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")  # Adjust model as needed

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/home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages/transformers/convert_slow_tokenizer.py:562: UserWarning: The sentencepiece tokenizer that you …

import requests
from bs4 import BeautifulSoup

page = requests.get('https://www.enchantedlearning.com/wordlist/adjectivesforpeople.shtml')
soup = BeautifulSoup(page.text, 'html.parser') 

items = soup.select('div.wordlist-item')

item_list = ''
for item in items:
    #print(item.get_text())
    item_list …

import numpy as np
import pandas as pd

# Check the student passed either math or passed at least in 2 subjects. If no condidition matched, consider them as failed
def pass_math_or_two_subjects(row):
    if(row.maths > 34):
        return …

from datetime import datetime

def get_age(d):
    d1 = datetime.now()
    months = (d1.year - d.year) * 12 + d1.month - d.month

    year = int(months / 12)
    return year

age = get_age(datetime(1991, 1, 1))

age

33

import gensim
from gensim import corpora
from pprint import pprint

# How to create a dictionary from a list of sentences?
documents = [
        """More than half of survey participants also reported clicking on a headline expecting …

import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo'],
    'points' : [80, 70, 90]
}

data

{'city': ['Toronto', 'Montreal', 'Waterloo'], 'points': [80, 70, 90]}

type(data)

dict

df = pd.DataFrame(data)

df

import numpy as np
import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo'],
    'points' : [80, 70, 90]
}

data

{'city': ['Toronto', 'Montreal', 'Waterloo'], 'points': [80, 70, 90]}

type(data)

dict

df = pd.DataFrame(data)

df

import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo', 'Toronto', 'Waterloo', 'Toronto', 'Toronto'],
    'points' : [80, 70, 90, 85, 79, 82, 200]
}

data

{'city': ['Toronto',
  'Montreal',
  'Waterloo',
  'Toronto',
  'Waterloo',
  'Toronto',
  'Toronto'],
 'points': [80, 70, 90, 85, 79, 82, 200]}

type(data)

dict

df = pd.DataFrame(data)

df

import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo', 'Toronto', 'Waterloo', 'Toronto', 'Toronto'],
    'points' : [80, 70, 90, 85, 79, 82, 200]
}

data

{'city': ['Toronto',
  'Montreal',
  'Waterloo',
  'Toronto',
  'Waterloo',
  'Toronto',
  'Toronto'],
 'points': [80, 70, 90, 85, 79 …

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]'

# !pip install argostranslate

print(pyu.ps2("argostranslate"))

argostranslate==1.9.6

import argostranslate.package
import argostranslate.translate

# Download and install the Finnish language package
from_code = "en …