Amourspirit

Running LibrePythonista in linuxserver/libreoffice docker

In order to install LibrePythonista in linuxserver/libreoffice docker (Alpine) image py3-pip must be installed. The other packages are required to install matplotlib.

Create a docker and a docker-compose.yml files and place them in the same folder. Use the contents from the examples below.

Note that volumes are optional. If you want to follow this yml file on Linux or Mac then create a folder in your home folder named vm_shared and that folder will be shared with you docker image.

volumes:

alexanderdavidsen

#!/usr/bin/env bash

# Colors
BLUE='\033[0;34m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
RED='\033[0;31m'
NC='\033[0m' # No Color
BOLD='\033[1m'

tdmalone

#!/usr/bin/env python

import json, sys
from urllib.request import urlopen


def green(str):  return f'\033[92m{str}\033[0m'
def orange(str): return f'\033[38;5;214m{str}\033[0m'
def red(str):    return f'\033[91m{str}\033[0m'
def yellow(str): return f'\033[93m{str}\033[0m'

jsoncow

justmarkham

MaxHalford

ustayready

import openai
import boto3
import json
import time
from typing import Dict, List

openai.api_key = '### SET YOUR OPENAPI API KEY HERE ###'
session = boto3.session.Session()
client = session.client('iam')

akhan619

Exploring Tokenizers from Hugging Face

Hugging Face (HF) has made NLP (Natural Language Processing) a breeze. In this post, we are going to take a look at tokenization using a hands on approach with the help of the Tokenizers library. We are going to load a real world dataset containing 10-K filings of public firms and see how to train a tokenizer from scratch based on the BERT tokenization scheme. In the process we will understand tokenization in detail and some gotchas to keep an eye out for.

Background on NLP (Optional)

If you already have an understanding of the NLP pipeline, you can safely skip this section.

For any NLP task, one of the first steps is pre-processing the data so that it can be fed into our NLP models. For those new to NLP, the general pipeline for any NLP task (text classification, question answering, etc.) is as follows:

rasbt

# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu

codewithbas

import json
from TwitterAPI import (
    TwitterAPI,
    TwitterPager
)

consumer_key        = "<YOUR API KEY>"
consumer_secret     = "<YOUR API KEY SECRET>"
access_token        = "<YOUR ACCESS TOKEN>"
access_token_secret = "<YOUR ACCESS TOKEN SECRET>"