def generate(model, inputs):
text = inputs['prompt']
encoded = _tokenize_article_pieces(encoder, text)
context_formatted = []
context_formatted.extend(encoded[:-1])
ignore_ids_np = np.array(encoder.special_tokens_onehot)
ignore_ids_np[encoder.endoftext] = 0
gens = []
gens_raw = []
gen_probs = []
for chunk_i in range(num_chunks):
tokens_out, probs_out = sess.run(
[model['tokens'], model['probs']],
feed_dict={
model['initial_context']:
[context_formatted] * batch_size_per_chunk,
model['eos_token']: 60000,
model['p_for_topp']: top_p[chunk_i]
})
for t_i, p_i in zip(tokens_out, probs_out):
extraction = extract_generated_target(output_tokens=t_i,
encoder=encoder,
target=args.target)
gens.append(extraction['extraction'])
return gens[0]
def generate_article_attribute(sess, encoder, tokens, probs, article, target='article'):
"""
Given attributes about an article (title, author, etc), use that context to generate
a replacement for one of those attributes using the Grover model.
This function is based on the Grover examples distributed with the Grover code.
"""
# Tokenize the raw article text
article_pieces = _tokenize_article_pieces(encoder, article)
# Grab the article elements the model careas about - domain, date, title, etc.
context_formatted = []
for key in ['domain', 'date', 'authors', 'title', 'article']:
if key != target:
context_formatted.extend(article_pieces.pop(key, []))
# Start formatting the tokens in the way the model expects them, starting with
# which article attribute we want to generate.
context_formatted.append(encoder.__dict__['begin_{}'.format(target)])
# Tell the model which special tokens (such as the end token) aren't part of the text
ignore_ids_np = np.array(encoder.special_tokens_onehot)
ignore_ids_np[encoder.__dict__['end_{}'.format(target)]] = 0
# We are only going to generate one article attribute with a fixed
# top_ps cut-off of 95%. This simple example isn't processing in batches.
gens = []
article['top_ps'] = [0.95]
# Run the input through the TensorFlow model and grab the generated output
tokens_out, probs_out = sess.run(
[tokens, probs],
feed_dict={
# Pass real values for the inputs that the
# model needs to be able to run.
initial_context: [context_formatted],
eos_token: encoder.__dict__['end_{}'.format(target)],
ignore_ids: ignore_ids_np,
p_for_topp: np.array([0.95]),
}
)
# The model is done! Grab the results it generated and format the results into normal text.
for t_i, p_i in zip(tokens_out, probs_out):
extraction = extract_generated_target(output_tokens=t_i, encoder=encoder, target=target)
gens.append(extraction['extraction'])
# Return the generated text.
return gens[-1]
# Format context end
# Indices we definitely DONT WANT TO PREDICT
ignore_ids_np = np.array(encoder.special_tokens_onehot)
ignore_ids_np[encoder.endoftext] = 0
gens = []
gens_raw = []
gen_probs = []
# article['top_ps'] = top_p.reshape(-1).tolist()
for chunk_i in range(num_chunks):
tokens_out, probs_out = sess.run([tokens, probs],
feed_dict={initial_context: [context_formatted] * batch_size_per_chunk,
eos_token: 60000,
p_for_topp: top_p[chunk_i]})
for t_i, p_i in zip(tokens_out, probs_out):
extraction = extract_generated_target(output_tokens=t_i, encoder=encoder, target=args.target)
gens.append(extraction['extraction'])
# article['gens_{}'.format(args.target)] = gens
# article['gensraw_{}'.format(args.target)] = gens_raw
# article['probs_{}'.format(args.target)] = gen_probs
# these were in there for whatever reason...
# article.pop('input_ids_conditional', None)
# article.pop('input_ids_unconditional', None)
# f_out.write(json.dumps(article) + '\n')
print(gens[0])
text = input()