def main():
model_path = '/home/warvisionary/parlai_transfer/from_pretrained/model'
print(f"Displaying example generation from model: {model_path}")
DisplayModel.main(task='empathetic_dialogues_ru',
model_file=model_path,
num_examples=16,
skip_generation=False)
def test_display_model(self):
from parlai.scripts.display_model import DisplayModel
with testing_utils.capture_output() as output:
DisplayModel.main(
model='fixed_response',
fixed_response='1 2 3 4',
task='integration_tests',
verbose=True,
)
output = output.getvalue()
assert 'metrics' in output
assert 'accuracy' in output
assert '1 2 3 4' in output
import sys
from parlai.scripts.display_model import DisplayModel
f = open("kv_self_short_0.4679.txt", 'w')
sys.stdout = f
DisplayModel.main(
task='personachat',
# model_file='model/seq2seq/self_persona_2',
model_file='model/kvmemnn/self_persona_short',
num_examples=1000,
)
f.close()
opt['datafile'] = opt['datatype'].split(':')[0] + ".txt"
super().__init__(opt, shared)
def setup_data(self, datafile):
# filename tells us where to load from.
# We'll just use some hardcoded data, but show how you could read the filename here:
print(f" ~~ Loading from {datafile} ~~ ")
# setup_data should yield tuples of ((text, label), new_episode)
# That is ((str, str), bool)
# first episode
# notice how we have call, response, and then True? The True indicates this is a first message
# in a conversation
# Let's discuss the security deposit</s>Do you have the full amount for the deposit?
yield ("Let's discuss the security deposit", ""), True
# Next we have the second turn. This time, the last element is False, indicating we're still going
yield ('Do you have the full amount for the deposit?', ''), False
# yield ("Let's say goodbye", ''), False
# second episode. We need to have True again!
yield (" Do you like baseball", ""), True
yield ("I've never watched a game.", ""), False
# yield ("Last chance", ""), False
DisplayData.main(task="chateval_multiturn")
DisplayModel.main(task='chateval_multiturn',
model_file='zoo:blender/blender_90M/model',
skip_generation=False)
from parlai.scripts.display_data import DisplayData
# DisplayData.main(task='wikiqa', num_examples=5)
from parlai.scripts.display_model import DisplayModel
DisplayModel.main(
task='bankQA',
model_file='from_pretrained/model',
num_examples=10,
skip_generation=False,
)
# from parlai.scripts.train_model import TrainModel
# print(TrainModel.help(model='seq2seq'))
new_episode = True
for line in f.readlines():
splits = line.split(' ')
if len(splits) == 3:
yield (splits[0].replace('text:', ''),
splits[1].replace('labels:', '')), new_episode
new_episode = True
else:
yield (splits[0].replace('text:', ''),
splits[1].replace('labels:', '')), new_episode
new_episode = False
DisplayModel.main(task='poly_teacher', model='poly')
TrainModel.main(
model='seq2seq',
model_file='poly-encoder/model',
dict_file='zoo:dodecadialogue/empathetic_dialogues_ft/model.dicts',
task='poly_teacher',
batchsize=3,
validation_every_n_secs=10,
max_train_time=60,
)
DisplayModel.main(
task='poly_teacher',
model_file='poly-encoder/model',
num_examples=6,
)