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

neubig

import requests
import sys
import time

sleep_time = 20
def query_api(url, session):
  global sleep_time
  time.sleep(sleep_time / 1000.0)
  r = session.get(url)
  while r.status_code == 429:

neubig

import openreview
import argparse
import requests
import time
import sys
import csv
import json
from tqdm import tqdm  # Progress bar

# This is a utility script to get a CSV of papers from semantic scholar given OpenReview ids

neubig

package edu.cmu.empty;

import com.intellij.openapi.actionSystem.AnAction;
import com.intellij.openapi.actionSystem.AnActionEvent;
import com.intellij.openapi.project.Project;
import com.intellij.openapi.ui.Messages;
import com.intellij.openapi.ui.popup.JBPopupFactory;
import com.intellij.openapi.ui.popup.ListPopup;
import com.intellij.openapi.ui.popup.PopupStep;
import com.intellij.openapi.ui.popup.util.BaseListPopupStep;

neubig

import sys
import re
from collections import defaultdict

# This is a script to identify pronouns in Japanese
# It requires data segmented by KyTea (http://www.phontron.com/kytea/)
#
# If you have raw Japanese text (with no spaces), use this script like:
#  cat japanese.txt | kytea | python identify_japanese_pronouns.py > japanese_with_pronouns.txt
#

neubig

#### Script to calculate the best paper deadline based on the population on earth based on some not-completely-arbitrary assumptions
# by Graham Neubig

# Results are:
# UTC 8:00 deadline, utility is 1476.1150000000002
# UTC 9:00 deadline, utility is 1438.7800000000002
# UTC 14:00 deadline, utility is 1385.2949999999998
# UTC 15:00 deadline, utility is 1345.945
# UTC 13:00 deadline, utility is 1291.4950000000003
# UTC 7:00 deadline, utility is 1287.1649999999997

neubig

openapi: "3.0.0"
info:
  version: 1.0.0
  title: Swagger Petstore
  license:
    name: MIT
servers:
  - url: http://petstore.swagger.io/v1
paths:
  /pets:

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

neubig

import numpy as np
import sys

################# Explanation ##################
# This is a function to calculate house prices h(x) = -40 + 0.25x
# The first term (-40) is the base price, and "x" is the number of square feet in the house
################################################

# Set up the function
my_function = np.array([-40, 0.25])

neubig

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# This is a simplified implementation of the LSTM language model (by Graham Neubig)
#
#  LSTM Neural Networks for Language Modeling
#  Martin Sundermeyer, Ralf Schlüter, Hermann Ney
#  InterSpeech 2012
# 
# The structure of the model is extremely simple. At every time step we