Make sure GPU is enabled (edit->notebook settings->Hardware Accelerator GPU)
!git clone https://github.com/nshepperd/gpt-2.git
cd /content/gpt-2/
! pip install -r requirements.txt
! python download_model.py 117M
cd /content/gpt-2/src
import json
import os
import numpy as np
import tensorflow as tf
import model, sample, encoder
raw_text = ''
def interact_model(
model_name='117M',
seed=None,
nsamples=1,
batch_size=1,
length=None,
temperature=1,
top_k=0,
):
"""
Interactively run the model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:nsamples=1 : Number of samples to return total
:batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples.
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
"""
global raw_text
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
enc = encoder.get_encoder(model_name)
hparams = model.default_hparams()
with open(os.path.join('models', model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams, length=length,
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k
)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
saver.restore(sess, ckpt)
# while True:
raw_text = input("Article title >>> ")
while not raw_text:
print('Title should not be empty!')
raw_text = input("Article title >>> ")
context_tokens = enc.encode(raw_text)
generated = 0
for _ in range(nsamples // batch_size):
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
for i in range(batch_size):
generated += 1
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
with open('out.txt', 'w') as out_file:
out_file.write(text)
print(text)
print("=" * 80)
cd ..
Enter the title of the fake news. (eg. Donald Trump was caught shoplifting from Abercrombie and Fitch on Hollywood Boulevard today.)
interact_model(top_k=40)
! pip install names
import names
import datetime
title = '# ' + raw_text[:-1]
repoter1 = names.get_full_name()
repoter2 = names.get_full_name()
time = datetime.datetime.now()
file = open('out.txt','r')
text = file.read()
idx = text.rfind('\n')
full_text = title + ' \n\n' + '**By ' + repoter1 + ' and ' + repoter2 + '** \n' + str(time)[:10] + ' \n\n' + '**(FNN) -**' + text[1:idx]
with open('fakenews.md', 'w') as out_file:
out_file.write(full_text)
The generated fake news is saved in fakenews.md