MLWhiz

import pandas as pd
import plotly.express as px

new_movies_list = movies_list*8
times_taken = []
for i in range(50, len(new_movies_list),50):
    print(i)
    movies_to_process = new_movies_list[:i]
    
    # Multiprocess:

MLWhiz

from multiprocessing import Pool
import time
import plotly.express as px
import plotly
import pandas as pd
from joblib import Parallel, delayed

def f(x):
    time.sleep(2)
    return x**2

MLWhiz

from multiprocessing import Pool
import time
import plotly.express as px
import plotly
import pandas as pd

def f(x):
    time.sleep(2)
    return x**2

MLWhiz

from multiprocessing import Pool
import time
import plotly.express as px
import plotly
import pandas as pd

def f(x):
    time.sleep(2)
    return x**2

MLWhiz

from multiprocessing import Pool
import time
import plotly.express as px
import plotly
import pandas as pd

def f(x):
    return x**2

def runner(list_length):

MLWhiz

class Solution:
    def minEatingSpeed(self, piles: List[int], h: int) -> int:
        # Can Koko finish the piles given this speed k?
        def check(k):
            hours_taken = 0
            for n in piles:
                if n%k==0:
                    hours_taken += n//k
                else:
                    hours_taken += n//k + 1

MLWhiz

def binary_search(array):
    def condition(value):
        pass
    # could be [0, n], [1, n] etc. Depends on problem
    left, right = min(search_space), max(search_space) 
    while left < right:
        mid = left + (right - left) // 2
        if condition(mid):
            right = mid
        else:

MLWhiz

text = r"""
George Washington (February 22, 1732[b] – December 14, 1799) was an American political leader, military general, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797. Previously, he led Patriot forces to victory in the nation's War for Independence. He presided at the Constitutional Convention of 1787, which established the U.S. Constitution and a federal government. Washington has been called the "Father of His Country" for his manifold leadership in the formative days of the new nation.
"""

question = "Who was the first president?"

inputs = tokenizer.encode_plus(question, text, add_special_tokens=True, return_tensors="pt")
input_ids = inputs["input_ids"].tolist()[0]

text_tokens = tokenizer.convert_ids_to_tokens(input_ids)

MLWhiz

model = AutoModelForQuestionAnswering.from_pretrained("test-squad-trained")
text = r"""
🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
TensorFlow 2.0 and PyTorch
"""
questions = [
    "How many pretrained models are available in Transformers?",
    "What does Transformers provide?",

MLWhiz

# Dealing with long docs:
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it 

def prepare_train_features(examples):
    # Tokenize our examples with truncation and padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    tokenized_examples = tokenizer(
        examples["question" ],