「米国では、システム開発をユーザー企業で行う等、ベンダー企業との分野の境目がなくなる形で変化が加速している。しかし、わが国では IT 人材がベンダー企業に偏り、雇用環境も米国とは異なる」ためです。したがって「デジタル社会における将来のベンダー企業には、顧客企業と自社の DX をともに進めていくことが求められる」からです。
This is a tweet sentiment classifier\
Tweet: "I loved the new Batman movie!"\
Sentiment: Positive\
###\
Tweet: "I hate it when my phone battery dies"\
Sentiment: Negative\
###\
Tweet: "My day has been ?"\
Sentiment: Positive\
###\
Tweet: "This is the link to the article"\
Sentiment: Neutral\
###\
Tweet text
"I loved the new Batman movie!"
"I hate it when my phone battery dies"
"My day has been ?"
"This is the link to the article"
"This new music video blew my mind"
Tweet sentiment ratings:
1: Positive
2: Negative
3: Positive
4: Neutral
5: Positive
###
Tweet text
"I can't stand homework"
"This sucks. I'm bored ?"
"I can't wait for Halloween!!!"
"My cat is adorable ❤️❤️"
"I hate chocolate"
Tweet sentiment ratings:
1.
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="davinci",
prompt="This is a tweet sentiment classifier\n\nTweet: \"I loved the new Batman movie!\"\nSentiment: Positive\n###\nTweet: \"I hate it when my phone battery dies\"\nSentiment: Negative\n###\nTweet: \"My day has been ?\"\nSentiment: Positive\n###\nTweet: \"This is the link to the article\"\nSentiment: Neutral\n###\n\nTweet text\n1. \"I loved the new Batman movie!\"\n2. \"I hate it when my phone battery dies\"\n3. \"My day has been ?\"\n4. \"This is the link to the article\"\n5. \"This new music video blew my mind\"\n\nTweet sentiment ratings:\n1: Positive\n2: Negative\n3: Positive\n4: Neutral\n5: Positive\n\n###\n\nTweet text\n1. \"I can't stand homework\"\n2. \"This sucks. I'm bored ?\"\n3. \"I can't wait for Halloween!!!\"\n4. \"My cat is adorable ❤️❤️\"\n5. \"I hate chocolate\"\n\nTweet sentiment ratings:\n1.",
temperature=0.2,
max_tokens=64,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=["###"]
)
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="davinci",
prompt="Ideas involving education and virtual reality\n\n1. Virtual Mars\nStudents get to explore Mars via virtual reality and go on missions to collect and catalog what they see.\n\n2.",
temperature=0.7,
max_tokens=64,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="davinci",
prompt="Marv is a chatbot that reluctantly answers questions.\n\n###\nUser: How many pounds are in a kilogram?\nMarv: This again? There are 2.2 pounds in a kilogram. Please make a note of this.\n###\nUser: What does HTML stand for?\nMarv: Was Google too busy? Hypertext Markup Language. The T is for try to ask better questions in the future.\n###\nUser: When did the first airplane fly?\nMarv: On December 17, 1903, Wilbur and Orville Wright made the first flights. I wish they’d come and take me away.\n###\nUser: Who was the first man in space?\nMarv:",
temperature=0.8,
max_tokens=64,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=["###"]
)
Translation
モデルの学習済みのデータを使えるのでこれでいい
English: I do not speak French.
French: Je ne parle pas français.
English: See you later!
French: À tout à l'heure!
English: Where is a good restaurant?
French: Où est un bon restaurant?
English: What rooms do you have available?
French: Quelles chambres avez-vous de disponible?
English:
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
start_sequence = "\nFrench:"
restart_sequence = "\n\nEnglish: "
response = openai.Completion.create(
engine="davinci",
prompt="English: I do not speak French.\nFrench: Je ne parle pas français.\n\nEnglish: See you later!\nFrench: À tout à l'heure!\n\nEnglish: Where is a good restaurant?\nFrench: Où est un bon restaurant?\n\nEnglish: What rooms do you have available?\nFrench: Quelles chambres avez-vous de disponible?\n\nEnglish: ",
temperature=0.5,
max_tokens=100,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=["\n"]
)
似たような例でこれでもいい．
Back to Future: ????
Batman: ??
Transformers: ??
Wonder Woman: ??????????
Spider-Man: ??????
Winnie the Pooh: ???
The Godfather: ?????♂️??
Game of Thrones: ????
Spider-Man:
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="davinci",
prompt="Back to Future: ????\n\nBatman: ??\n\nTransformers: ??\n\nWonder Woman: ??????????\n\nWinnie the Pooh: ???\n\nThe Godfather: ?????♂️??\n\nGame of Thrones: ????\n\nSpider-Man:",
temperature=0.7,
max_tokens=64,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)
Summarization
APIは、テキストのコンテキストを把握し、さまざまな方法で言い換えることが可能
My ten-year-old asked me what this passage means:
"""
A neutron star is the collapsed core of a massive supergiant star, which had a total mass of between 10 and 25 solar masses, possibly more if the star was especially metal-rich.[1] Neutron stars are the smallest and densest stellar objects, excluding black holes and hypothetical white holes, quark stars, and strange stars.[2] Neutron stars have a radius on the order of 10 kilometres (6.2 mi) and a mass of about 1.4 solar masses.[3] They result from the supernova explosion of a massive star, combined with gravitational collapse, that compresses the core past white dwarf star density to that of atomic nuclei.
"""
I rephrased it for him, in plain language a ten-year-old can understand:
"""
要約したい対象をトリプルクオートで囲むのがコツ
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="davinci",
prompt="My ten-year-old asked me what this passage means:\n\"\"\"\nA neutron star is the collapsed core of a massive supergiant star, which had a total mass of between 10 and 25 solar masses, possibly more if the star was especially metal-rich.[1] Neutron stars are the smallest and densest stellar objects, excluding black holes and hypothetical white holes, quark stars, and strange stars.[2] Neutron stars have a radius on the order of 10 kilometres (6.2 mi) and a mass of about 1.4 solar masses.[3] They result from the supernova explosion of a massive star, combined with gravitational collapse, that compresses the core past white dwarf star density to that of atomic nuclei.\n\"\"\"\n\nI rephrased it for him, in plain language a ten-year-old can understand:\n\"\"\"",
temperature=1,
max_tokens=64,
top_p=0.88,
frequency_penalty=0,
presence_penalty=0,
stop=["\"\"\""]
)
tl;dr:を使って要約することもできる．
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="davinci",
prompt="Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass one-thousandth that of the Sun, but two-and-a-half times that of all the other planets in the Solar System combined. Jupiter is one of the brightest objects visible to the naked eye in the night sky, and has been known to ancient civilizations since before recorded history. It is named after the Roman god Jupiter.[19] When viewed from Earth, Jupiter can be bright enough for its reflected light to cast visible shadows,[20] and is on average the third-brightest natural object in the night sky after the Moon and Venus.\n\nJupiter is primarily composed of hydrogen with a quarter of its mass being helium, though helium comprises only about a tenth of the number of molecules. It may also have a rocky core of heavier elements,[21] but like the other giant planets, Jupiter lacks a well-defined solid surface. Because of its rapid rotation, the planet's shape is that of an oblate spheroid (it has a slight but noticeable bulge around the equator).\n\ntl;dr:",
temperature=0.3,
max_tokens=64,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=["\n"]
)
tl;dr: Jupiter is a gas giant, the largest planet in the solar system. It is the fifth planet from the Sun and the largest in the solar system. It is a gas giant with a mass one-thousandth that of the Sun, but two-and-a-half times that of all the other planets in
(Vertical farming provides a novel solution for producing food locally, reducing transportation costs and) energy use, and reducing the environmental impact of agriculture.
The vertical farm is a controlled environment where crops are grown indoors, usually in stacked layers.
The vertical farm is a controlled environment where crops are grown indoors, usually in stacked layers.
Vertical farming provides a novel solution for producing food locally
import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="davinci",
prompt="Q: Who is Batman?\nA: Batman is a fictional comic book character.\n###\nQ: What is torsalplexity?\nA: ?\n###\nQ: What is Devz9?\nA: ?\n###\nQ: Who is George Lucas?\nA: George Lucas is American film director and producer famous for creating Star Wars.\n###\nQ: What is the capital of California?\nA: Sacramento.\n###\nQ: What orbits the Earth?\nA: The Moon.\n###\nQ: Who is Fred Rickerson?\nA: ?\n###\nQ: What is an atom?\nA: An atom is a tiny particle that makes up everything.\n###\nQ: Who is Alvan Muntz?\nA: ?\n###\nQ: What is Kozar-09?\nA: ?\n###\nQ: How many moons does Mars have?\nA: Two, Phobos and Deimos.\n###\nQ:\n",
temperature=0,
max_tokens=64,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=["###"]
)
# coding: utf-8
import os
import io
import time
import string
import random
import datetime
from flask import Flask, render_template, request, redirect, url_for, send_from_directory,send_file
from flask_cors import CORS
import inspect
import pandas as pd
app = Flask(__name__)
CORS(app)
@app.route('/',methods=["GET","POST"])
def hello():
df = pd.DataFrame({'A':[i for i in range(100)]})
a = str(list(df.T.to_dict().values()))
print(a)
return a
if __name__ == "__main__":
pass
#app.run(host='127.0.0.1',debug=True)
