rosslazer

import json
import os
from collections import deque

root_dir = '~/slack_messages'

target_user = "UMQN69N8M"
res = []

for dirpath, dirnames, filenames in os.walk(root_dir):

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

jangedoo

import re
def get_tokens_with_entities(raw_text: str):
    raw_tokens = re.split(r"\s(?![^\[]*\])", raw_text)
    entity_value_pattern = r"\[(?P<value>.+?)\]\((?P<entity>.+?)\)"
    entity_value_pattern_compiled = re.compile(entity_value_pattern, flags=re.I|re.M)

    tokens_with_entities = []

    for raw_token in raw_tokens:
        match = entity_value_pattern_compiled.match(raw_token)

dhh

ruby '2.7.1'

gem 'rails', github: 'rails/rails'
gem 'tzinfo-data', '>= 1.2016.7'  # Don't rely on OSX/Linux timezone data

# Action Text
gem 'actiontext', github: 'basecamp/actiontext', ref: 'okra'
gem 'okra', github: 'basecamp/okra'

# Drivers

BarclayII

import pandas as pd
import dgl
import os
import torch

class MovieLens(object):
    def __init__(self, directory):
        '''
        directory: path to movielens directory which should have the three
                   files:

theriley106

import sys
reload(sys)
sys.setdefaultencoding("utf-8")
import requests
import bs4
import zipcode
import threading
import re
import json
import time

bitgord

// package.json
{
  "dependencies": {
    "web3": "0.17.0-alpha",
    "solc": "^0.4.4"
  }
}

//Create file Ecrow.sol and create 3 variables: a buyer, a seller, and an arbiter
contract Escrow {

vterron

#! /usr/bin/env python

# Author: Victor Terron (c) 2015
# Email: `echo vt2rron1iaa32s | tr 132 @.e`
# License: GNU GPLv3

""" A memcached function decorator. """

from __future__ import division
from __future__ import print_function

CloudCray

"""
Author: Cloud Cray
Date:   2014-01-04
 
This is an example script to read Zip Code data from a US shapefile,
  then colorize all zip codes, then save the result to a png file.
"""
 
import shapefile
import matplotlib.patches as patches

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t