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import os | |
import re | |
from datetime import datetime | |
import errno | |
def titleToUrl(title): | |
lowerTitle = title.lower() | |
URL = lowerTitle.replace(" ", "-") | |
return "/blog/" + URL + "/" |
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''' | |
This file is a helper function to scrape Github for Repo's to add to my website. | |
It can be set to run whenever Gatsby is called in order to add new Repo's as they arrive. | |
Usage: python RepoScraper.py SampleOutput.js | |
''' | |
from sys import argv | |
from github import Github | |
import re |
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def brand_from_url(url): | |
domain_list = urlparse(url).netloc.split('.') | |
if(len(domain_list) < 3): | |
brand= domain_list[0] | |
else: | |
brand = domain_list[1] | |
return brand |
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import boto3 | |
def get_id_from_name(name): | |
description = [instance for r in response['Reservations'] for instance in r['Instances'] for tag in instance['Tags'] if tag['Key'] == 'Name' if tag['Value'] == name] | |
return description['InstanceId'] | |
def start_instance_by_name(name): | |
ec2 = boto3.client('ec2') | |
instance_id = get_id_from_name(name) | |
# Do a dryrun first to verify permissions |
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# This function takes Open Graph info and just returns the articles | |
def returnArticles(og_array): | |
article_urls = [] | |
for og in og_array: | |
is_article = False | |
for prop in og: | |
if hasattr(prop, "property"): | |
if prop["property"] == "og:type": | |
if prop["content"] == "article": | |
is_article = True |
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# i/p = array of numbers | |
# create a binary tree such that each subtree is a min-heap and the inorder traversal // of the binary tree is same as the array provided | |
# [5, 7, 10, 8, 1, 4] | |
# 1 | |
# / \ | |
# 5 4 | |
# \ | |
# 7 |
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// Disable bold. | |
term_.prefs_.set('enable-bold', false) | |
// Use this for Zenburn | |
term_.prefs_.set('background-color', "#3F3F3F"); | |
term_.prefs_.set('foreground-color', "#DCDCCC"); | |
base03 = "#002b36"; | |
base02 = "#073642"; | |
base01 = "#586e75"; |
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#!/usr/bin/env python | |
"""Counts the number of times a word occurs in a very large text file""" | |
from __future__ import print_function | |
import os | |
import sys | |
import argparse | |
import textacy | |
import multiprocessing | |
from tqdm import tqdm |
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from gensim import utils | |
def save2gensim(fname, word2vec_dict): | |
vectors = list(word2vec_dict.values()) | |
vector_size = vectors[0].shape[0] | |
total_vec = len(vectors) | |
with utils.smart_open(fname, 'wb') as fout: | |
fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size))) | |
# store in sorted order: most frequent words at the top | |
for word, vector in word2vec_dict.items(): |
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# See https://huggingface.co/docs/datasets/upload_dataset for more details | |
from datasets import load_dataset | |
dataset_name = "PUT_YOUR_NAME_HERE" | |
data_files = {"train": "train.csv", "dev": "dev.csv", "test": "test.csv"} | |
dataset = load_dataset("namespace/your_dataset_name", data_files=data_files) | |
datasets.push_to_hub(f"SALT-NLP/{dataset_name}", private=True) |
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