hugobowne

class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1

iwouldnot

import asyncio
import json
from time import time, sleep

import aiohttp
import pandas as pd
from bs4 import BeautifulSoup
from tqdm import tqdm

# Ващет СИКРЕТНА!!!!! Но код хранится в private репозитории, так что пох

MrEliptik

import re, string, unicodedata
import nltk
import contractions
import inflect
from nltk import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import LancasterStemmer, WordNetLemmatizer

def replace_contractions(text):
    """Replace contractions in string of text"""

zenius

'''
Tic-Tac-Toe Console Application
@author 'zenius lama'
@Version 3.5.3
@Since 2020-06-06
'''

from random import randrange

#

Monsieur-Chat

TryHackMe | DNS in Detail | Writeup

TryHackMe - DNS in Detail - Writeup DNS in Detail is a room created by adamtlangley.

Answers/Flags :

What is DNS?

| Question | Answer |

rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

init27

# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models