def interactive_test():
starting_temp = 40 #0.4 * 100
ending_temp = 60 #0.6 * 100
temp_incr = 10 #0.1 & 100
for i in range(starting_temp, ending_temp, temp_incr):
temp = i / 100.0
interact_model(temperature=temp)
print("Finished")
def handleMainProcess(toDel, c, strdata, jointpacketError, client):
if len(jointpacketError) == 2:
strdata = "/m" + jointpacketError[1]
sds = strdata.split("\n")
#parse and open params to send for gpt
seed = int(sds[2])
if sds[1] == "false":
seed = random.randint(0, 2**32 - 1)
lenth = int(sds[3])
temp = float(sds[4])
top_k = int(sds[5])
output = gpt.interact_model(sds[6], sds[0][2:], seed, 1, 1,
lenth if lenth != 0 else None, temp, top_k)
newdata = output
c.sendto(newdata.encode('utf-8'), client.addr)
if toDel in clients:
id = toDel.id
clients.remove(toDel)
print("Removed request " + str(id))
async def on_message(message):
if message.author.id != 699023487188074562: # <-- JargoBot's ID.
if message.content == "!obliviate":
await message.channel.send(
"*Forgets everything*\nHmm... Must have hit my head...")
Jargobot.obliviate()
Jargobot.dump_memory()
else:
await message.channel.trigger_typing()
Jargobot.remember(message.content)
response = interact_model(
prompt=Jargobot.recollections).splitlines()[0]
while response == "<|endoftext|>":
await message.channel.trigger_typing()
print("TRYING AGAIN! GOT ENDOFTEXT.")
response = interact_model(
prompt=Jargobot.recollections).splitlines()[0]
await message.channel.send(response)
Jargobot.remember(response)
Jargobot.dump_memory()
def generate_responses(text): #,sess, gpt2):
print('Generating responses')
responses = interact_model(text + ' ||| ',
top_k=40,
temperature=0.8,
nsamples=10,
model_name='355M')
# responses = gpt2.generate(sess,
# length=280,
# temperature=0.8,
# prefix=text+' ||| ',
# nsamples=10,
# batch_size=5,
# return_as_list=True)
print(responses)
return responses
def get_text_messages(message):
answer_text = " ".join(message.text.lower().split()[:-1])
bot.send_message(
message.from_user.id,
interact_model(input_text=answer_text,
length=int(message.text.split()[-1])))
def main():
allComments = extractAllComments()
comments = interact_model(comments=allComments)
postCommentHandler(comments)
if loss < best_loss:
best_loss = loss
loss = train(sess, data_test, is_train=False)
print(" PPL on testing set:", loss)
saver.save(sess,
'%s/checkpoint' % train_dir,
global_step=global_step.eval())
print("saving parameters in %s" % train_dir)
else:
if FLAGS.cond:
print("begin conditionally generating stories......")
interact_model(
sess=sess,
enc=enc,
PAD_ID=PAD_ID,
hparams=hparams,
context=context,
dataset=
data_test, # Accept console input if `dataset` is set to None
output_file_name="./inference_gpt2.txt",
temperature=FLAGS.temperature,
top_k=FLAGS.top_k)
else:
print("begin unconditionally generating stories......")
sample_model(sess=sess,
enc=enc,
PAD_ID=PAD_ID,
hparams=hparams,
temperature=FLAGS.temperature,
top_k=FLAGS.top_k)
print("end generating stories......")
import interactive_conditional_samples as ics
import json
summary = 'this is a random phrase that will be used as a prompt for the model to use, in hope for it to generate more content that has both substance and fluff'
smpls = ics.interact_model(summary)
samplesJson = json.dumps(smpls)
file = open('sample.txt', 'w')
file.write(samplesJson)
file.close()
for samp_strat, values in vals_dict.items():
for val in values:
if samp_strat=='tfs':
alpha_set=val # this is actually now a probability threshold
elif samp_strat=='n':
nuc_prob_set=val
elif samp_strat=='flat':
flat_set=val
else:
top_k_set=val
interact_model( # some other variables are initialized below
general_path = '',
alpha=alpha_set,
nuc_prob=nuc_prob_set,
flat_prob = flat_set,
sampler=samp_strat, #n, k or tfs
pre_prepared_prompts = True,
num_prepared_prompts_wanted = 100, #5000
model_name='774M', # '345M',
seed=27,
batch_size=25, # 500
generated_length=150,
prompt_length = 100,
temperature=1,
top_k=top_k_set,
models_dir='../gpt-2/models',
)