GPT-3, GPT-J ve GPT-Neo çok güçlü yapay zeka modelleridir. Burada size bu modelleri birkaç atışlık öğrenme sayesinde nasıl etkili bir şekilde kullanabileceğinizi gösteriyoruz. Az sayıda örnekle öğrenme, isteminizde birkaç örnek vererek bir yapay zeka modelini eğitmek/ince ayar yapmak gibidir.
OpenAI tarafından piyasaya sürülen GPT-3, metin anlama ve metin oluşturma için şimdiye kadar piyasaya sürülen en güçlü yapay zeka modelidir.
175 milyar parametre üzerinde eğitilmiştir, bu da onu son derece çok yönlü ve hemen hemen her şeyi anlayabilir hale getirir!
GPT-3 ile sohbet robotları, içerik oluşturma, varlık çıkarma, sınıflandırma, özetleme ve çok daha fazlası gibi her türlü şeyi yapabilirsiniz. Ancak biraz pratik gerektirir ve bu modeli doğru kullanmak kolay değildir.
GPT-Neo ve GPT-J'nin her ikisi de açık kaynaklı Doğal Dil İşleme modelleridir ve bir kolektif tarafından oluşturulmuştur. araştırmacılar açık kaynak yapay zeka için çalışıyor (EleutherAI'nin web sitesine bakın).
GPT-J 6 milyar parametreye sahiptir, bu da onu en gelişmiş açık kaynaklı Doğal Dil İşleme modeli bu yazı itibariyle. Bu, OpenAI'nin tescilli GPT-3 Curie'sine doğrudan bir alternatiftir.
Bu modeller çok yönlüdür. Neredeyse tüm Doğal Dil İşleme kullanım durumları için kullanılabilirler: metin oluşturma, duyarlılık Analiz, sınıflandırma, makine çevirisi, ... ve çok daha fazlası (aşağıya bakınız). Ancak bunları etkili bir şekilde kullanmak bazen pratik gerektirir. Yanıt süreleri (gecikme) de daha standart Doğal Dil İşleme yöntemlerinden daha uzun olabilir modeller.
GPT-J ve GPT-Neo'nun her ikisi de NLP Cloud API'sinde mevcuttur. Aşağıda, elde edilen örnekleri gösteriyoruz
kullanarak
Python istemcisi ile GPU üzerinde NLP Cloud'un GPT-J uç noktası. Örnekleri kopyalayıp yapıştırmak isterseniz,
Lütfen
kendi API token'ınızı eklemeyi unutmayın. Python istemcisini yüklemek için önce aşağıdakileri çalıştırın: pip install nlpcloud.
Birkaç atışla öğrenme, bir makine öğrenimi modelinin yalnızca birkaç atış sayesinde tahminler yapmasına yardımcı olmakla ilgilidir. Örnekler. Burada yeni bir model eğitmeye gerek yoktur: GPT-3, GPT-J ve GPT-Neo gibi modeller o kadar büyüktür ki yeniden eğitilmeden birçok bağlama kolayca adapte olabilir.
Modele yalnızca birkaç örnek vermek, doğruluğunu önemli ölçüde artırmasına yardımcı olur.
Doğal Dil İşleme'de fikir, bu örnekleri metin girdinizle birlikte iletmektir. Aşağıdaki örneklere bakın!
Ayrıca, birkaç atışlık öğrenme yeterli değilse, OpenAI'nin web sitesinde GPT-3'e ve NLP Cloud'da GPT-J'ye ince ayar yapabileceğinizi unutmayın, böylece model kullanım durumunuza mükemmel şekilde uyarlanmıştır.
NLP Cloud oyun alanında birkaç vuruşluk öğrenmeyi kolayca test edebilirsiniz (burada deneyin).
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Message: Support has been terrible for 2 weeks...
Sentiment: Negative
###
Message: I love your API, it is simple and so fast!
Sentiment: Positive
###
Message: GPT-J has been released 2 months ago.
Sentiment: Neutral
###
Message: The reactivity of your team has been amazing, thanks!
Sentiment:""",
min_length=1,
max_length=1,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])Çıktı:
PositiveGördüğünüz gibi, önce uygun bir formatla 3 örnek vermemiz, GPT-J'nin şunları anlamasına yol açıyor Duygu analizi yapmak istediğimizi. Ve sonucu iyi.
GPT-J'nin farklı uygulamaları anlamasına yardımcı olabilirsiniz
bölümlerini aşağıdaki gibi özel bir sınırlayıcı kullanarak ayırabilirsiniz: ###. Bunun gibi başka bir şeyi mükemmel bir şekilde kullanabiliriz: ---. Ya da sadece yeni bir
Hat. Daha sonra bir NLP Cloud parametresi olan "end_sequence" ayarlanır.
GPT-J'ye yeni bir satırdan sonra içerik oluşturmayı durdurmasını söyler + ###: end_sequence="###".
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""description: a red button that says stop
code: <button style=color:white; background-color:red;>Stop</button>
###
description: a blue box that contains yellow circles with red borders
code: <div style=background-color: blue; padding: 20px;><div style=background-color: yellow; border: 5px solid red; border-radius: 50%; padding: 20px; width: 100px; height: 100px;>
###
description: a Headline saying Welcome to AI
code:""",
max_length=500,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])Çıktı:
<h1 style=color: white;>Welcome to AI</h1>GPT-J ile kod üretimi gerçekten şaşırtıcı. Bunun nedeni kısmen GPT-J'nin devasa üzerinde eğitilmiş kod tabanları.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Question: Fetch the companies that have less than five people in it.
Answer: SELECT COMPANY, COUNT(EMPLOYEE_ID) FROM Employee GROUP BY COMPANY HAVING COUNT(EMPLOYEE_ID) < 5;
###
Question: Show all companies along with the number of employees in each department
Answer: SELECT COMPANY, COUNT(COMPANY) FROM Employee GROUP BY COMPANY;
###
Question: Show the last record of the Employee table
Answer: SELECT * FROM Employee ORDER BY LAST_NAME DESC LIMIT 1;
###
Question: Fetch three employees from the Employee table;
Answer:""",
max_length=100,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
SELECT * FROM Employee ORDER BY ID DESC LIMIT 3;Otomatik SQL oluşturma, özellikle SQL'in bildirimsel doğası nedeniyle GPT-J ile çok iyi çalışır ve SQL'in nispeten az sayıda olasılığa sahip oldukça sınırlı bir dil olduğu gerçeği (çoğu programlama dilleri).
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""[Text]: Fred is a serial entrepreneur. Co-founder and CEO of Platform.sh, he previously co-founded Commerce Guys, a leading Drupal ecommerce provider. His mission is to guarantee that as we continue on an ambitious journey to profoundly transform how cloud computing is used and perceived, we keep our feet well on the ground continuing the rapid growth we have enjoyed up until now.
[Name]: Fred
[Position]: Co-founder and CEO
[Company]: Platform.sh
###
[Text]: Microsoft (the word being a portmanteau of "microcomputer software") was founded by Bill Gates on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. Steve Ballmer replaced Gates as CEO in 2000, and later envisioned a "devices and services" strategy.
[Name]: Steve Ballmer
[Position]: CEO
[Company]: Microsoft
###
[Text]: Franck Riboud was born on 7 November 1955 in Lyon. He is the son of Antoine Riboud, the previous CEO, who transformed the former European glassmaker BSN Group into a leading player in the food industry. He is the CEO at Danone.
[Name]: Franck Riboud
[Position]: CEO
[Company]: Danone
###
[Text]: David Melvin is an investment and financial services professional at CITIC CLSA with over 30 years’ experience in investment banking and private equity. He is currently a Senior Adviser of CITIC CLSA.
""",
top_p=0,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
[Name]: David Melvin
[Position]: Senior Adviser
[Company]: CITIC CLSA
Gördüğünüz gibi, GPT-J yapılandırılmamış metinden yapılandırılmış veri çıkarma konusunda çok başarılı. GPT-J'nin herhangi bir yeniden eğitime bile ihtiyaç duymadan varlık çıkarma işlemini çözmesi gerçekten etkileyici! Genellikle, Yeni varlık türlerinin (isim, pozisyon, ülke vb.) çıkarılması tamamen yeni bir süreç gerektirir. açıklama, eğitim, dağıtım... Burada her şey tamamen sorunsuz.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Context: NLP Cloud was founded in 2021 when the team realized there was no easy way to reliably leverage Natural Language Processing in production.
Question: When was NLP Cloud founded?
Answer: 2021
###
Context: NLP Cloud developed their API by mid-2020 and they added many pre-trained open-source models since then.
Question: What did NLP Cloud develop?
Answer: API
###
Context: All plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.
Question: When can plans be stopped?
Answer: Anytime
###
Context: The main challenge with GPT-J is memory consumption. Using a GPU plan is recommended.
Question: Which plan is recommended for GPT-J?
Answer:""",
min_length=1,
max_length=20,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
GPU planıSoru yanıtlama çok iyi çalışıyor. Diğer özel Doğal Dil İşleme modelleri ile de elde edilebilir, ancak belki aynı değil doğruluk seviyesi.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Here is a tutorial about how to make a cake.
1. Take some flour.
2. Take some sugar.""",
max_length=500)
print(generation["generated_text"])
Çıktı:
Here is a tutorial how to make a cake.
1. Take some flour.
2. Take some sugar.
3. Take some butter.
4. Take some eggs.
5. Take some water.
6. Take some baking powder.
7. Take some vanilla.
8. Mix all together.
9. Bake in a pan.
10. Enjoy.
Well, that's it. You can make this for your birthday or a party or you can even make it for your kids. They will love this.Gördüğünüz gibi GPT-J'nin ilk biçimlendirmenizi otomatik olarak nasıl takip ettiği oldukça etkileyici ve üretilen içerik de çok iyi. Bundan düzgün bir pasta yaratabilirsiniz (henüz denemedim Yine de).
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""I love goin to the beach.
Correction: I love going to the beach.
###
Let me hav it!
Correction: Let me have it!
###
It have too many drawbacks.
Correction: It has too many drawbacks.
###
I do not wan to go
Correction:""",
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
Gitmek istemiyorum.Yazım ve dilbilgisi düzeltmeleri beklendiği gibi çalışır. Konum hakkında daha spesifik olmak istiyorsanız Cümledeki hatayı düzeltmek için özel bir model kullanmak isteyebilirsiniz.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Hugging Face a révolutionné le NLP.
Translation: Hugging Face revolutionized NLP.
###
Cela est incroyable!
Translation: This is unbelievable!
###
Désolé je ne peux pas.
Translation: Sorry but I cannot.
###
NLP Cloud permet de deployer le NLP en production facilement.
Translation:""",
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
NLP Cloud makes it easy to deploy NLP to production.Makine çevirisi genellikle özel modeller kullanır (genellikle her dil için 1 tane). Burada tüm diller ele alınır GPT-J tarafından kutudan çıkarılır, ki bu oldukça etkileyicidir.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""keyword: markets
tweet: Take feedback from nature and markets, not from people
###
keyword: children
tweet: Maybe we die so we can come back as children.
###
keyword: startups
tweet: Startups should not worry about how to put out fires, they should worry about how to start them.
###
keyword: NLP
tweet:""",
max_length=200,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
People want a way to get the benefits of NLP without paying for it.İşte bir bağlamı takip ederek kısa tweetler oluşturmanın eğlenceli ve kolay bir yolu.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""This is a discussion between a [human] and a [robot].
The [robot] is very nice and empathetic.
[human]: Hello nice to meet you.
[robot]: Nice to meet you too.
###
[human]: How is it going today?
[robot]: Not so bad, thank you! How about you?
###
[human]: I am ok, but I am a bit sad...
[robot]: Oh? Why that?
###
[human]: I broke up with my girlfriend...
[robot]:""",
min_length=1,
max_length=20,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
Oh? How did that happen?Gördüğünüz gibi, GPT-J konuşma modunda olduğunuzu doğru bir şekilde anlıyor. Ve çok güçlü Mesele şu ki, bağlamınızdaki tonu değiştirirseniz, modelden gelen yanıtlar da aynı şekilde olacaktır. tonu (alay, öfke, merak...).
Aslında bir chatbotun nasıl oluşturulacağı hakkında özel bir blog makalesi yazdık GPT-3/GPT-J, okumaktan çekinmeyin!
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""I want to start coding tomorrow because it seems to be so fun!
Intent: start coding
###
Show me the last pictures you have please.
Intent: show pictures
###
Search all these files as fast as possible.
Intent: search files
###
Can you please teach me Chinese next week?
Intent:""",
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
learn chineseGPT-J'nin cümlenizden niyeti nasıl algılayabildiği oldukça etkileyici. Daha fazlası için çok iyi çalışıyor karmaşık cümleler. Hatta ondan şunu bile isteyebilirsiniz İsterseniz amacı farklı şekilde biçimlendirin. Örneğin otomatik olarak bir Javascript oluşturabilirsiniz "learnChinese" gibi bir fonksiyon adı.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""[Original]: Algeria recalled its ambassador to Paris on Saturday and closed its airspace to French military planes a day later after the French president made comments about the northern Africa country.
[Paraphrase]: Last Saturday, the Algerian government recalled its ambassador and stopped accepting French military airplanes in its airspace. It happened one day after the French president made comments about Algeria.
###
[Original]: President Macron was quoted as saying the former French colony was ruled by a "political-military system" with an official history that was based not on truth, but on hatred of France.
[Paraphrase]: Emmanuel Macron said that the former colony was lying and angry at France. He also said that the country was ruled by a "political-military system".
###
[Original]: The diplomatic spat came days after France cut the number of visas it issues for citizens of Algeria and other North African countries.
[Paraphrase]: Diplomatic issues started appearing when France decided to stop granting visas to Algerian people and other North African people.
###
[Original]: After a war lasting 20 years, following the decision taken first by President Trump and then by President Biden to withdraw American troops, Kabul, the capital of Afghanistan, fell within a few hours to the Taliban, without resistance.
[Paraphrase]:""",
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True,
min_length=0,
max_length=50)
print(generation["generated_text"])
Çıktı:
French President Emmanuel Macron hopes the diplomatic tension with Algeria will calm down. He wants to make progress in the relations with the former French colony. President Macron says he has a good relationship with his Algerian counterpart and he is glad that they have a cordial relationship. The president was quoted as saying that Algeria was ruled by a "political-military system" with an official history that was based not on truth, but on hatred of France. France cut the number of visas it issues for citizens of Algeria and other North African countries.GPT-J paragrafımızda değişiklikler yaptı, ancak ana anlamı korudu, ki bu da paraphrasing ile ilgilidir. GPT-J'yi daha fazla orijinal paraphrase döndürmesi için mükemmel bir şekilde teşvik edebilirsiniz Girdide farklı örnekler geçirerek ve sıcaklık, top_p gibi API parametreleriyle oynayarak, tekrarlama cezası...
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""[Original]: America has changed dramatically during recent years. Not only has the number of graduates in traditional engineering disciplines such as mechanical, civil, electrical, chemical, and aeronautical engineering declined, but in most of the premier American universities engineering curricula now concentrate on and encourage largely the study of engineering science. As a result, there are declining offerings in engineering subjects dealing with infrastructure, the environment, and related issues, and greater concentration on high technology subjects, largely supporting increasingly complex scientific developments. While the latter is important, it should not be at the expense of more traditional engineering.
Rapidly developing economies such as China and India, as well as other industrial countries in Europe and Asia, continue to encourage and advance the teaching of engineering. Both China and India, respectively, graduate six and eight times as many traditional engineers as does the United States. Other industrial countries at minimum maintain their output, while America suffers an increasingly serious decline in the number of engineering graduates and a lack of well-educated engineers.
(Source: Excerpted from Frankel, E.G. (2008, May/June) Change in education: The cost of sacrificing fundamentals. MIT Faculty
[Summary]: MIT Professor Emeritus Ernst G. Frankel (2008) has called for a return to a course of study that emphasizes the traditional skills of engineering, noting that the number of American engineering graduates with these skills has fallen sharply when compared to the number coming from other countries.
###
[Original]: So how do you go about identifying your strengths and weaknesses, and analyzing the opportunities and threats that flow from them? SWOT Analysis is a useful technique that helps you to do this.
What makes SWOT especially powerful is that, with a little thought, it can help you to uncover opportunities that you would not otherwise have spotted. And by understanding your weaknesses, you can manage and eliminate threats that might otherwise hurt your ability to move forward in your role.
If you look at yourself using the SWOT framework, you can start to separate yourself from your peers, and further develop the specialized talents and abilities that you need in order to advance your career and to help you achieve your personal goals.
[Summary]: SWOT Analysis is a technique that helps you identify strengths, weakness, opportunities, and threats. Understanding and managing these factors helps you to develop the abilities you need to achieve your goals and progress in your career.
###
[Original]: 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.
Jupiter 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).
[Summary]: Jupiter is the largest planet in the solar system. It is a gas giant, and is the fifth planet from the sun.
###
[Original]: For all its whizz-bang caper-gone-wrong energy, and for all its subsequent emotional troughs, this week’s Succession finale might have been the most important in its entire run. Because, unless I am very much wrong, Succession – a show about people trying to forcefully mount a succession – just had its succession. And now everything has to change.
The episode ended with Logan Roy defying his children by selling Waystar Royco to idiosyncratic Swedish tech bro Lukas Matsson. It’s an unexpected twist, like if King Lear contained a weird new beat where Lear hands the British crown to Jack Dorsey for a laugh, but it sets up a bold new future for the show. What will happen in season four? Here are some theories.
Season three of Succession picked up seconds after season two ended. It was a smart move, showing the immediate swirl of confusion that followed Kendall Roy’s decision to undo his father, and something similar could happen here. This week’s episode ended with three of the Roy siblings heartbroken and angry at their father’s grand betrayal. Perhaps season four could pick up at that precise moment, and show their efforts to reorganise their rebellion against him. This is something that Succession undoubtedly does very well – for the most part, its greatest moments have been those heart-thumping scenes where Kendall scraps for support to unseat his dad – and Jesse Armstrong has more than enough dramatic clout to centre the entire season around the battle to stop the Matsson deal dead in its tracks.
[Summary]:""",
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True,
min_length=20,
max_length=200)
print(generation["generated_text"])
Çıktı:
Season 3 of Succession ended with Logan Roy trying to sell his company to Lukas Matsson.Metin özetleme zor bir iştir. GPT-J bu konuda çok iyidir, yeter ki siz ona doğru Örnekler. Özetin boyutu ve özetin tonu, büyük ölçüde verdiğiniz örneklere bağlıdır. oluşturulmuştur. Örneğin, aynı türden örnekler oluşturmayabilirsiniz, ister bir Çocuklar için basit bir özet veya doktorlar için gelişmiş bir tıbbi özet. GPT-J'nin girdi boyutu özetleme örnekleriniz için çok küçükse, özetleme göreviniz için GPT-J'ye ince ayar yapmak isteyebilirsiniz.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Message: When the spaceship landed on Mars, the whole humanity was excited
Topic: space
###
Message: I love playing tennis and golf. I'm practicing twice a week.
Topic: sport
###
Message: Managing a team of sales people is a tough but rewarding job.
Topic: business
###
Message: I am trying to cook chicken with tomatoes.
Topic:""",
min_length=1,
max_length=5,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
foodİşte "sıfır çekim" denilen yöntem sayesinde bir metni kategorize etmenin kolay ve güçlü bir yolu öğrenme" tekniğiyle, kategorileri önceden bildirmek zorunda kalmadan.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Information Retrieval (IR) is the process of obtaining resources relevant to the information need. For instance, a search query on a web search engine can be an information need. The search engine can return web pages that represent relevant resources.
Keywords: information, search, resources
###
David Robinson has been in Arizona for the last three months searching for his 24-year-old son, Daniel Robinson, who went missing after leaving a work site in the desert in his Jeep Renegade on June 23.
Keywords: searching, missing, desert
###
I believe that using a document about a topic that the readers know quite a bit about helps you understand if the resulting keyphrases are of quality.
Keywords: document, understand, keyphrases
###
Since transformer models have a token limit, you might run into some errors when inputting large documents. In that case, you could consider splitting up your document into paragraphs and mean pooling (taking the average of) the resulting vectors.
Keywords:""",
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
paragraphs, transformer, input, errorsAnahtar kelime çıkarma, bir metin parçasından ana fikirleri elde etmekle ilgilidir. Bu ilginç bir Doğal Dil İşleme yöntemidir. GPT-J'nin çok iyi işleyebildiği alt alan. Anahtar kelime çıkarma için aşağıya bakın (aynı şey ancak birden fazla kelime).
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Information Retrieval (IR) is the process of obtaining resources relevant to the information need. For instance, a search query on a web search engine can be an information need. The search engine can return web pages that represent relevant resources.
Keywords: information retrieval, search query, relevant resources
###
David Robinson has been in Arizona for the last three months searching for his 24-year-old son, Daniel Robinson, who went missing after leaving a work site in the desert in his Jeep Renegade on June 23.
Keywords: searching son, missing after work, desert
###
I believe that using a document about a topic that the readers know quite a bit about helps you understand if the resulting keyphrases are of quality.
Keywords: document, help understand, resulting keyphrases
###
Since transformer models have a token limit, you might run into some errors when inputting large documents. In that case, you could consider splitting up your document into paragraphs and mean pooling (taking the average of) the resulting vectors.
Keywords:""",
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
large documents, paragraph, mean poolingYukarıdaki ile aynı örnek, ancak bu sefer tek bir kelime değil, birkaç kelime çıkarmak istiyoruz (anahtar kelime olarak adlandırılır).
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""Generate a product description out of keywords.
Keywords: shoes, women, $59
Sentence: Beautiful shoes for women at the price of $59.
###
Keywords: trousers, men, $69
Sentence: Modern trousers for men, for $69 only.
###
Keywords: gloves, winter, $19
Sentence: Amazingly hot gloves for cold winters, at $19.
###
Keywords: t-shirt, men, $39
Sentence:""",
min_length=5,
max_length=30,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
Extraordinary t-shirt for men, for $39 only.GPT-J'den belirli anahtar kelimeleri içeren bir ürün açıklaması veya reklam oluşturmasını istemek mümkündür. İşte burada Biz sadece Basit bir cümle oluşturuyoruz, ancak gerekirse bütün bir paragrafı kolayca oluşturabiliriz.
import nlpcloud
client = nlpcloud.Client("gpt-j", "your_token", gpu=True)
generation = client.generation("""[Title]: 3 Tips to Increase the Effectiveness of Online Learning
[Blog article]: <h1>3 Tips to Increase the Effectiveness of Online Learning</h1>
<p>The hurdles associated with online learning correlate with the teacher’s inability to build a personal relationship with their students and to monitor their productivity during class.</p>
<h2>1. Creative and Effective Approach</h2>
<p>Each aspect of online teaching, from curriculum, theory, and practice, to administration and technology, should be formulated in a way that promotes productivity and the effectiveness of online learning.</p>
<h2>2. Utilize Multimedia Tools in Lectures</h2>
<p>In the 21st century, networking is crucial in every sphere of life. In most cases, a simple and functional interface is preferred for eLearning to create ease for the students as well as the teacher.</p>
<h2>3. Respond to Regular Feedback</h2>
<p>Collecting student feedback can help identify which methods increase the effectiveness of online learning, and which ones need improvement. An effective learning environment is a continuous work in progress.</p>
###
[Title]: 4 Tips for Teachers Shifting to Teaching Online
[Blog article]: <h1>4 Tips for Teachers Shifting to Teaching Online </h1>
<p>An educator with experience in distance learning shares what he’s learned: Keep it simple, and build in as much contact as possible.</p>
<h2>1. Simplicity Is Key</h2>
<p>Every teacher knows what it’s like to explain new instructions to their students. It usually starts with a whole group walk-through, followed by an endless stream of questions from students to clarify next steps.</p>
<h2>2. Establish a Digital Home Base</h2>
<p>In the spirit of simplicity, it’s vital to have a digital home base for your students. This can be a district-provided learning management system like Canvas or Google Classrooms, or it can be a self-created class website. I recommend Google Sites as a simple, easy-to-set-up platform.</p>
<h2>3. Prioritize Longer, Student-Driven Assignments</h2>
<p>Efficiency is key when designing distance learning experiences. Planning is going to take more time and require a high level of attention to detail. You will not be able to correct mistakes on the fly or suddenly pivot when kids are disengaged.</p>
<h2>4. Individual Touchpoints Are Game-Changers</h2>
<p>You can create these touchpoints through any medium you like: emails, video messages, phone calls, messages through your learning management system, comments on shared documents, etc.</p>
###
[Title]: 3 Best Free Image Converter Software Programs
[Blog article]: <h1>3 Best Free Image Converter Software Programs</h1>
<p>Best free image converters for JPG, BMP, PSD, TIF, GIF, RAW, and more</p>
<h2>1. XnConvert</h2>
<p>XnConvert is the Swiss Army knife of image converters. With XnView, you can convert any of around 500 image formats to your choice of around 80 others. If you have a rare image format that you can't open, XnView can probably convert it.</p>
<h2>2. CoolUtils Online Image Converter</h2>
<p>The CoolUtils Online Image Converter is just that—an image converter that exists completely online, no download required. Unlike some online image converters, this one does the image converting for you in real-time—no waiting on an email link.</p>
<h2>3. FileZigZag</h2>
<p>FileZigZag is another online image converter service that will convert most common graphics formats. Just upload the original image, choose the desired output, and then wait for the download link to appear on the page.</p>
###
[Title]: 4 Best Tips to Find a Girlfriend
[Blog article]: """,
max_length=500,
length_no_input=True,
end_sequence="###",
remove_end_sequence=True,
remove_input=True)
print(generation["generated_text"])
Çıktı:
<h1>Text Generation API</h1>
<h2>What is Text Generation?</h2>
<p>Text generation is the process of generating text from a block of text. It is not simply the inverse of summarization: a text generator will take a short piece of text as an input and generate a longer one.</p>
<p>For example you have the following text:</p>
Ahead of the opening of the meeting,
<p>In this case the goal of the API is to automatically generate the following:</p>
Ahead of the opening of the meeting, Prime Minister Justin Trudeau spoke to reporters at the Montreal airport where he was travelling to speak at the conference.
<p>So basically we want to leverage the output of the API as the input for the text generator, and we will then be able to generate some sentences based on the input we already have.</p>
<h2>Text generation with GPT-2</h2>
<p>We will leverage GPT-2 in order to have a starting point for the text generator. In this case, we will use the API as the input for the text generator, and we will use the output of the API as the input for GPT-2.
However, we want to fine-tune GPT-2 to generate more complex sentences and to have better results.</p>Etkileyici değil mi? Oluşturulan bu blog makalesi küçük ama siz çok daha uzun makaleler oluşturabilirsiniz. Bu Oluşturulan blog yazısının yapısı gerçekten de birkaç fotoğraflık örneklerinizde kullandığınız yapıya bağlıdır. Daha karmaşık yapılar ve daha alakalı içerik elde etmek için GPT-J'de ince ayar yapmak çok önemlidir.
Gördüğünüz gibi, az atışla öğrenme GPT-3, GPT-J ve GPT-Neo'nun inanılmaz sonuçlar elde etmesine yardımcı olan harika bir tekniktir. şeyler! Buradaki kilit nokta, talebinizi yapmadan önce doğru bir bağlam iletmektir.
Basit metin üretimi için bile, aşağıdakilere yardımcı olmak için mümkün olduğunca fazla bağlam aktarılması önerilir model.
Umarım faydalı bulmuşsunuzdur! Bu modellerden en iyi şekilde nasıl yararlanabileceğiniz hakkında sorularınız varsa, lütfen bize sormaktan çekinmeyin.
Julien Salinas
NLP Cloud'da CTO