NewscatcherAPI

summary = [article['summary'] for article in articles]
sentence = summary[0]

parth-trambadiya

import logging
import base64
import boto3
import os

logger = logging.getLogger()
logger.setLevel(logging.INFO)

s3_client = boto3.client('s3')

aquilax

<a href="javascript:(function(){Array.from(document.querySelectorAll('textarea')).map(function(b){var a=document.createElement('div');var d=document.createElement('button');d.textContent='↑';d.addEventListener('click',function(f){f.preventDefault();b.value=Array.from(new Set(b.value.split('\n'))).sort().join('\n')});var c=document.createElement('button');c.textContent='↓';c.addEventListener('click',function(f){f.preventDefault();b.value=Array.from(new Set(b.value.split('\n'))).sort().reverse().join('\n')});a.appendChild(d);a.appendChild(c);b.parentNode.insertBefore(a,b)})})();">Sort textarea unique</a>

aquilax

<a href="javascript:(function(){Array.from(document.querySelectorAll('textarea')).map(function(b){var a=document.createElement('div');var d=document.createElement('button');d.textContent='↑';d.addEventListener('click',function(f){f.preventDefault();b.value=b.value.split('\n').sort().join('\n')});var c=document.createElement('button');c.textContent='↓';c.addEventListener('click',function(f){f.preventDefault();b.value=b.value.split('\n').sort().reverse().join('\n')});a.appendChild(d);a.appendChild(c);b.parentNode.insertBefore(a,b)})})();">Sort textareas</a>

aquilax

javascript:(function(){
var container = document.createElement('div');
container.setAttribute('style', 'position: absolute; top: 0; right: 0; left: 0; bottom: 0; z-index: 9999');

var textarea = document.createElement('textarea');
textarea.setAttribute('style', 'width:99%; height: 20em');

var close = document.createElement('button');
close.textContent = 'Close'
close.addEventListener("click", function() {

juanarrivillaga

import pandas as pd
df = pd.read_csv('en_bigram.csv')
st = df[df["right"] == "some_text"]["left"]
st[st.str.startswith("My")].to_list()


# using a dict reader
import csv
result = []
with open("en_bigram.csv") as f:

ameerkat

MineRobber9000

###########################################################
# How to NEVER use lambdas. An inneficient and yet educa- #
# tonal [sic] guide to the proper misuse of the lambda    #
# construct in Python 3.x.  [DO NOT USE ANY OF THIS EVER] #
#          original by (and apologies to): e000 (13/6/11) #
#           now in Python 3 courtesy of: khuxkm (17/9/20) #
###########################################################

## Part 1. Basic LAMBDA Introduction ##
# If you're reading this, you've probably already read e000's

davidkyle

Getting started with the Inference Aggregation

First ingest the customer churn data and trained model

• calls.csv
• customers.csv

Ingest the linked csv files above into Elastic using the Kibana Data Visualizer. You will find the Data Visualizer in the Machine Learning section. Ingest into the indices calls and customers.

• telco_churn_model.json

miladfa7

from simpletransformers.language_modeling import LanguageModelingModel
import logging


logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)


train_args = {