To get started, you need to install the following libraries:pip3 install tqdm numpy tensorflow==2.0.0 sklearn CopyNow open up a new Python notebook or file and follow along. Let's import our necessary modules:from tqdm import tqdm from tensorflow.keras.preprocessing.sequence import pad_...
Let's get started; we will be using thetarfilebuilt-in module, so we don't have to install anything; you can optionally installtqdmjust for printing progress bars: pip3 install tqdm Copy Open up a new Python file and: importtarfilefromtqdmimporttqdm# pip3 install tqdm ...
from tqdm import tqdm for i in tqdm(range(0,100)): pass # do nothing Output: In this code, we first import the tqdm library. Then we write a normal for loop to iterate over arangeobject, except that we pass therangeobject inside the methodtqdmof thetqdmmodule. As the for loop canno...
To get started, we need quite a few dependencies, let's install them:pip3 install requests bs4 tqdm CopyOpen up a new Python file and import necessary modules:import requests import os from tqdm import tqdm from bs4 import BeautifulSoup as bs from urllib.parse import urljoin, urlparse Copy...
1 ! pip install -qU datasets sentence-transformers numpy pandas tqdm Additionally for Voyage AI: voyageai: Python library to interact with OpenAI APIs 1 Additionally for OpenAI: openai: Python library to interact with OpenAI APIs 1 ! pip install -qU openai Additionally for UAE: transformers: ...
importtorchfromIPython.displayimportImage# for displaying imagesimportosimportrandomimportshutilfromsklearn.model_selectionimporttrain_test_splitimportxml.etree.ElementTreeasETfromxml.domimportminidomfromtqdmimporttqdmfromPILimportImage,ImageDrawimportnumpyasnpimportmatplotlib.pyplotasplt ...
import numpy as np import pickle import tqdm from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Dropout, Activation import os sequence_length = 100 # dataset file path FILE_PATH = "data/wonderland.txt" # FILE_PATH = "data/python_code.py" BASENAME...
15 # Initialize Embedding for transforming raw documents to vectors** 16 from langchain.embeddings import HuggingFaceEmbeddings 17 from tqdm import tqdm as notebook_tqdm 18 19 embeddings = HuggingFaceEmbeddings() 20 21 # Initialize MongoDB client along with Langchain connector modu...
from tqdm import tqdm def port_scan(target, scan_type): # Code for getting user input and importing modules try: if scan_type.lower() == ‘tcp’: # TCP scanning logic target_ports = range(1, 100) # Example – Modify it as needed ...
importtorchfromIPython.displayimportImage# for displaying imagesimportosimportrandomimportshutilfromsklearn.model_selectionimporttrain_test_splitimportxml.etree.ElementTreeasETfromxml.domimportminidomfromtqdmimporttqdmfromPILimportImage,ImageDrawimportnumpyasnpimportmatplotlib.pyplotasplt ...