mobilemind

# delete local tag '12345'
git tag -d 12345
# delete remote tag '12345' (eg, GitHub version too)
git push origin :refs/tags/12345
# alternative approach
git push --delete origin tagName
git tag -d tagName

MInner

from tqdm import tqdm_notebook as tqdm
from joblib import Parallel, delayed
import time

import random

def func(x):
    time.sleep(random.randint(1, 10))
    return x

niranjv

# A virtualenv running Python3.6 on Amazon Linux/EC2 (approximately) simulates the Python 3.6 Docker container used by Lambda
# and can be used for developing/testing Python 3.6 Lambda functions
# This script installs Python 3.6 on an EC2 instance running Amazon Linux and creates a virtualenv running this version of Python
# This is required because Amazon Linux does not come with Python 3.6 pre-installed 
# and several packages available in Amazon Linux are not available in the Lambda Python 3.6 runtime

# The script has been tested successfully on a t2.micro EC2 instance (Root device type: ebs; Virtualization type: hvm)
# running Amazon Linux AMI 2017.03.0 (HVM), SSD Volume Type - ami-c58c1dd3
# and was developed with the help of AWS Support

johnhw

### JHW 2018
import numpy as np
import umap



# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
    
import random

arfon

# Install go
$ sudo apt-get install golang-go

# Download goofys package
$ go get github.com/kahing/goofys

# Install goofys
$ go install github.com/kahing/goofys

# Copy the goofys binary to somewhere in your path

tamuhey

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:

• repo: https://github.com/tamuhey/tokenizations

alexalouit

# be careful, goofys does not support fsync, software performing check will return an error

$ wget https://github.com/kahing/goofys/releases/latest/download/goofys
$ chmod +x goofys
$ mv goofys /usr/bin/goofys

$ mkdir /root/.aws
$ echo -e "[default]\naws_access_key_id = $accesskey\naws_secret_access_key = $secretkey" > /root/.aws/credentials
$ echo 'goofys#bucket /mnt/bucket-name fuse _netdev,allow_other,--endpoint=https://s3.fr-par.scw.cloud/,--file-mode=0755,--dir-mode=0755,--uid=65534,--gid=65534 0 0' >> /etc/fstab