mmsamiei

bits 32 
	jmp start
start:
		push ebx
		mov ecx, 0x30
		mov eax, dword [fs: ecx];	EAX = Address Of PEB
		mov eax, [eax + 0xc];		EAX = Address Of PEB->Ldr
		mov esi, [eax + 0x14];		ESI = PEB->Ldr.InMemOrder
		lodsd;						EAX = Second module
		xchg eax, esi;				EAX = ESI, ESI = EAX

mmsamiei

mmsamiei

mmsamiei

!pip install stanfordnlp
import stanfordnlp
stanfordnlp.download('fa')
nlp = stanfordnlp.Pipeline(processors = "tokenize", lang="fa", models_dir=".")
str = "برای رفتن به سمرقند باید از خان‌های بی‌شمار گذشت"
doc = nlp(str)
for sent in doc.sentences:
  for wrd in sent.words:
    print(wrd.text)

mmsamiei

from torchtext.data import BucketIterator, interleave_keys

batch_size = 32

def batch_size_fn(new, count, sofar):
    "Keep augmenting batch and calculate total number of tokens + padding."
    # (new example to add, current effective batch size, current count of examples in the batch) 
    # when returned value meets batch_size (effective, innate effective batch_size 
    # defined as global bala bala) then wraper create a batch

mmsamiei

[
{"page": "Mathematics"},
{"page": "Mathematician"},
{"page": "Arithmetic"},
{"page": "Addition"},
{"page": "Subtraction"},
{"page": "Multiplication"},
{"page": "Division (mathematics)"},
{"page": "Euclidean algorithm"},
{"page": "Fraction (mathematics)"},

mmsamiei

import json                              
                                         
freader = open('test_random_split.json') 
data = json.load(freader)                
                                         
with open('correct-sample.json', 'w') as 
    for entry in data:                   
        json.dump(entry, outfile)        
        outfile.write('\n')              

mmsamiei

import json
from pprint import pprint
from tqdm import tqdm

freader = open('test_random_split.json')
data = json.load(freader)

print(len(data))

new_dataset = []

mmsamiei

df['polarity'] = df['Text'].map(lambda text: textblob.TextBlob(text).sentiment.polarity)
df['review_len'] = df['Text'].astype(str).apply(len)
df['word_count'] = df['Text'].apply(lambda x: len(str(x).split()))

mmsamiei

! wget -q "https://drive.google.com/uc?export=download&id=1-3tnHTdDjtMd9O2LgKN2ir3t5KvnqrXI" -O dataset.zip
! unzip dataset.zip

import subprocess
import shlex

file_id = "1xhiGDTihHYUbGES88sYt4S6nLDjKEji1"
file_name = "mscoco.zip"
url_get_cookie = f"https://drive.google.com/uc?export=download&id={file_id}"