$ pip install transformers

from transformers import pipeline

# download & load GPT-2 model
gpt2_generator = pipeline('text-generation', model='gpt2')

# generate 3 different sentences
# results are sampled from the top 50 candidates
sentences = gpt2_generator("To be honest, neural networks", do_sample=True, top_k=50, temperature=0.6, max_length=128, num_return_sequences=3)
for sentence in sentences:
  print(sentence["generated_text"])
  print("="*50)

Setting pad_token_id to eos_token_id:50256 for open-end generation.
To be honest, neural networks are still not very powerful. So they can't perform a lot of complex tasks like predicting the future — and they can't do that. They're just not very good at learning.

Instead, researchers are using the deep learning technology that's built into the brain to learn from the other side of the brain. It's called deep learning, and it's pretty straightforward.

For example, you can do a lot of things that are still not very well understood by the outside world. For example, you can read a lot of information, and it's a lot easier to understand what the person
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To be honest, neural networks are not perfect, but they are pretty good at it. I've used them to build a lot of things. I've built a lot of things. I'm pretty good at it.

When we talk about what we're doing with our AI, it's kind of like a computer is going to go through a process that you can't see. It's going to have to go through it for you to see it. And then you can see it. And you can see it. It's going to be there for you. And then it's going to be there for you to see it
==================================================
To be honest, neural networks are going to be very interesting to study for a long time. And they're going to be very interesting to read, because they're going to be able to learn a lot from what we've learned. And this is where the challenge is, and this is also something that we've been working on, is that we can take a neural network and make a neural network that's a lot simpler than a neural network, and then we can do that with a lot more complexity.

So, I think it's possible that in the next few years, we may be able to make a neural network that
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# download & load GPT-J model! It's 22.5GB in size
gpt_j_generator = pipeline('text-generation', model='EleutherAI/gpt-j-6B')

# generate sentences with TOP-K sampling
sentences = gpt_j_generator("To be honest, robots will", do_sample=True, top_k=50, temperature=0.6, max_length=128, num_return_sequences=3)
for sentence in sentences:
  print(sentence["generated_text"])
  print("="*50)

Setting pad_token_id to eos_token_id:50256 for open-end generation.
To be honest, robots will never replace humans.

The reason is simple: We humans are far more complex than the average robot.

The human brain is a marvel of complexity, capable of performing many thousands of tasks simultaneously. There are over 100 billion neurons in the human brain, with over 10,000 connections between each neuron, and neurons are capable of firing over a million times per second.

We have a brain that can reason, learn, and remember things. We can learn and retain information for a lifetime. We can communicate, collaborate, and work together to achieve goals. We can learn languages, play instruments
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To be honest, robots will probably replace many human jobs.

That's the prediction of a team of economists from the University of Oxford.

They say that in the future we can expect to see more robots doing jobs that are tedious, repetitive and dangerous.

Even something as simple as a housekeeper could be a thing of the past.

The researchers also think that robots will become cheaper and more versatile over time.

The idea of a robot housekeeper has been a popular one.

One company has already started a trial by offering a robot to take care of your home.

But the
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To be honest, robots will never replace the human workforce. It’s not a matter of if, but when. I can’t believe I’m writing this, but I’m glad I am.

Let’s start with what robots do. Robots are a form of technology. There’s a difference between a technology and a machine. A machine is a physical object designed to perform a specific task. A technology is a system of machines.

For example, a machine can be used for a specific purpose, but it still requires humans to operate it. A technology can be
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# generate Python Code!
print(gpt_j_generator(
"""
import os
# make a list of all african countries
""",
    do_sample=True, top_k=10, temperature=0.05, max_length=256)[0]["generated_text"])

import os
# make a list of all african countries
african_countries = ['Algeria', 'Angola', 'Benin', 'Burkina Faso', 'Burundi', 'Cameroon', 'Cape Verde', 'Central African Republic', 'Chad', 'Comoros', 'Congo', 'Democratic Republic of Congo', 'Djibouti', 'Egypt', 'Equatorial Guinea', 'Eritrea', 'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Kenya', 'Lesotho', 'Liberia', 'Libya', 'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius', 'Morocco', 'Mozambique', 'Namibia', 'Niger', 'Nigeria', 'Rwanda', 'Sao Tome and Principe', 'Senegal', 'Sierra Leone', 'Somalia', 'South Africa', 'South Sudan', 'Sudan', 'Swaziland', 'Tanzania', 'Togo', 'Tunisia',

print(gpt_j_generator(
"""
import cv2

image = "image.png"

# load the image and flip it
""",
    do_sample=True, top_k=10, temperature=0.05, max_length=256)[0]["generated_text"])

import cv2

image = "image.png"

# load the image and flip it
img = cv2.imread(image)
img = cv2.flip(img, 1)

# resize the image to a smaller size
img = cv2.resize(img, (100, 100))

# convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# threshold the image to get the black and white image
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)

# show the image
cv2.imshow("thresh", thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

# complete bash script!
print(gpt_j_generator(
"""
# get .py files in /opt directory
ls *.py /opt
# get public ip address
""", max_length=256, top_k=50, temperature=0.05, do_sample=True)[0]["generated_text"])

# get .py files in /opt directory
ls *.py /opt
# get public ip address
ip addr show dev eth0 | grep inet | awk '{print $2}' | cut -d/ -f1
# get mac address
ip link show dev eth0 | grep HWaddr | awk '{print $2}' | cut -d/ -f1
# get ip address
ip addr show dev eth0 | grep inet | awk '{print $2}' | cut -d/ -f1

# generating bash script!
print(gpt_j_generator(
"""
# update the repository
sudo apt-get update
# install and start nginx
""", max_length=128, top_k=50, temperature=0.1, do_sample=True)[0]["generated_text"])

# update the repository
sudo apt-get update
# install and start nginx
sudo apt-get install nginx
sudo service nginx start

# create a new user
sudo useradd -m -s /bin/bash -G sudo -u www-data www-data
# add the user to the sudoers file
sudo visudo
# add the user to the www-data group
sudo usermod -aG www-data www-data
# add the user to the www-data group
sudo usermod -aG www-data www-data
# add the

# Java code!
print(gpt_j_generator(
"""
public class Test {

public static void main(String[] args){
  // printing the first 20 fibonacci numbers
""", max_length=256, top_k=50, temperature=0.1, do_sample=True)[0]["generated_text"])

public class Test {

public static void main(String[] args){
  // printing the first 20 fibonacci numbers
  for(int i=0;i<20;i++){
    System.out.println(fibonacci(i));
  }
}

public static int fibonacci(int n){
  if(n<2){
    return n;
  }
  else{
    return fibonacci(n-1) + fibonacci(n-2);
  }
}
}

A:

You need to return the value from the recursive call.
public static int fibonacci(int n){
  if(n<2){
    return n;
  }
  else{
    return fibonacci(n-1) + fibonacci(n-2);
  }
}

A:

You need to return the value from the recursive call.
public static int fibonacci(int n){
  if(n<2){
    return n;
  }
  else{

# LATEX!
print(gpt_j_generator(
r"""
% list of Asian countries
\begin{enumerate}
""", max_length=128, top_k=15, temperature=0.1, do_sample=True)[0]["generated_text"])

% list of Asian countries
\begin{enumerate}
\item \textbf{India}
\item \textbf{China}
\item \textbf{Japan}
\item \textbf{Korea}
\item \textbf{Taiwan}
\item \textbf{Hong Kong}
\item \textbf{Singapore}
\item \textbf{Malaysia}
\item \textbf{Indonesia}
\item \textbf{Philippines}
\item \textbf{Thailand}
\item \text