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

vjache

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alvations

The Project Gutenberg EBook of The Adventures of Sherlock Holmes
by Sir Arthur Conan Doyle
(#15 in our series by Sir Arthur Conan Doyle)

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ogrisel

#
# XXX: do not use this code, it's broken!
# Use: https://gist.github.com/ogrisel/b6a97ed87939e3b559568ac2f6599cba
#
# See comments.

import os
import os.path as op
from time import time
import dask.dataframe as ddf

neubig

"""
DyNet implementation of a sequence labeler (POS taggger).
This is a translation of this tagger in PyTorch: https://gist.github.com/hal3/8c170c4400576eb8d0a8bd94ab231232

Basic architecture:
 - take words
 - run though bidirectional GRU
 - predict labels one word at a time (left to right), using a recurrent neural network "decoder"
The decoder updates hidden state based on:
 - most recent word

hal3

"""
PyTorch implementation of a sequence labeler (POS taggger).

Basic architecture:
 - take words
 - run though bidirectional GRU
 - predict labels one word at a time (left to right), using a recurrent neural network "decoder"

The decoder updates hidden state based on:
 - most recent word

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.

meew0

# This script parses a dump of Bulbapedia's Pokémon pages into a JSON file
# with details about what Pokémon are obtainable in respective regions
# (specifically, the latest series of games set in a specific region).

require 'nokogiri'
require 'json'

# An XML dump of all of Bulbapedia's Pokémon pages is required to exist at
# this path. It can be generated using this special page:
#   http://bulbapedia.bulbagarden.net/wiki/Special:Export

narphorium