mculp

Agnes               en_US    # Isn't it nice to have a computer that will talk to you?
Albert              en_US    #  I have a frog in my throat. No, I mean a real frog!
Alex                en_US    # Most people recognize me by my voice.
Alice               it_IT    # Salve, mi chiamo Alice e sono una voce italiana.
Alva                sv_SE    # Hej, jag heter Alva. Jag är en svensk röst.
Amelie              fr_CA    # Bonjour, je m’appelle Amelie. Je suis une voix canadienne.
Anna                de_DE    # Hallo, ich heiße Anna und ich bin eine deutsche Stimme.
Bad News            en_US    # The light you see at the end of the tunnel is the headlamp of a fast approaching train.
Bahh                en_US    # Do not pull the wool over my eyes.
Bells               en_US    # Time flies when you are having fun.

banaslee

eXtreme Go Horse (XGH) Process

Source: http://gohorseprocess.wordpress.com


1. I think therefore it's not XGH.
In XGH you don't think, you do the first thing that comes to your mind. There's not a second option as the first one is faster.

2. There are 3 ways of solving a problem: the right way, the wrong way and the XGH way which is exactly like the wrong one but faster.
XGH is faster than any development process you know (see Axiom 14).

loretoparisi

Alex                en_US    # Most people recognize me by my voice.
Alice               it_IT    # Salve, mi chiamo Alice e sono una voce italiana.
Alva                sv_SE    # Hej, jag heter Alva. Jag är en svensk röst.
Amelie              fr_CA    # Bonjour, je m’appelle Amelie. Je suis une voix canadienne.
Anna                de_DE    # Hallo, ich heiße Anna und ich bin eine deutsche Stimme.
Carmit              he_IL    # שלום. קוראים לי כרמית, ואני קול בשפה העברית.
Damayanti           id_ID    # Halo, nama saya Damayanti. Saya berbahasa Indonesia.
Daniel              en_GB    # Hello, my name is Daniel. I am a British-English voice.
Diego               es_AR    # Hola, me llamo Diego y soy una voz española.
Ellen               nl_BE    # Hallo, mijn naam is Ellen. Ik ben een Belgische stem.

ngpestelos

May 8, 2018

I wrote this four years ago, so instead use this command:

$ docker rmi $(docker images -q -f dangling=true)

neubig

# NOTE:
# You can find an updated, more robust and feature-rich implementation
# in Zeno Build
# - Zeno Build: https://github.com/zeno-ml/zeno-build/
# - Implementation: https://github.com/zeno-ml/zeno-build/blob/main/zeno_build/models/providers/openai_utils.py

import openai
import asyncio
from typing import Any

yoavg

Some remarks on Large Language Models

Yoav Goldberg, January 2023

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.

Intro

Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

jamiew

easterEgg.BadWorder.list={
"4r5e":1,
"5h1t":1,
"5hit":1,
a55:1,
anal:1,
anus:1,
ar5e:1,
arrse:1,
arse:1,

feelinc

#!/usr/bin/python

import os
import sys
import boto3

# get an access token, local (from) directory, and S3 (to) directory
# from the command-line
local_directory, bucket, destination = sys.argv[1:4]

thbkrkr

[
  { "keys": ["f12"], "command": "htmlprettify"},
  { "keys": ["f1"], "command": "fold" },
  { "keys": ["f2"], "command": "unfold" },
  { "keys": ["ctrl+à"], "command": "show_overlay", "args": {"overlay": "goto", "text": "@"} },
  { "keys": ["ctrl+!"], "command": "show_overlay", "args": {"overlay": "goto", "text": ":"} },
  { "keys": ["ctrl+space"], "command": "auto_complete" },
  { "keys": ["ctrl+space"], "command": "replace_completion_with_auto_complete", "context":
    [
      { "key": "last_command", "operator": "equal", "operand": "insert_best_completion" },

nschneid

# http://nlp.cs.nyu.edu/wiki/corpuswg/AnnotationCompatibilityReport
# Table 1: Part of Speech Compatibility					
# (Initial Version from Manning and Schutz 1998, pp. 141-142)					
# Extended to cover Claws1 and ICE
# cf. http://www.scs.leeds.ac.uk/ccalas/tagsets/brown.html
# Nathan Schneider, 2011-02-19:
# * Fixed some errors in brown column, e.g.: DT1 => DTI, PP0 => PPO, NRS => NPS
# * Added last column (Twitter tagset) and several special tags at the end
Category	Examples	Claws c5, Claws1	Brown	PTB	ICE	Twitter
Adjective	happy, bad	AJ0	JJ	JJ	ADJ.ge	A