def interactive(opt, print_parser=None):
if print_parser is not None:
if print_parser is True and isinstance(opt, ParlaiParser):
print_parser = opt
elif print_parser is False:
print_parser = None
if isinstance(opt, ParlaiParser):
print(
'[ Deprecated Warning: interactive should be passed opt not Parser ]'
)
opt = opt.parse_args()
opt['task'] = 'self_feeding'
build(opt)
opt['task'] = 'parlai.agents.local_human.local_human:LocalHumanAgent'
cand_file = os.path.join(opt['datapath'], 'self_feeding/convai2_cands.txt')
# Set values to override when the opt dict for the saved model is loaded
opt['override'] = {
'subtasks': ['dialog', 'satisfaction'],
'interactive': True,
'interactive_task': True,
'prev_response_filter': True,
'person_tokens':
False, # SelfFeedingAgent adds person_tokens on its own
'partial_load': True,
'history_size': 2,
'eval_candidates': 'fixed',
'encode_candidate_vecs': True,
'fixed_candidates_path': cand_file,
# Pull these from current opt dictionary
'no_cuda': opt["no_cuda"],
'fixed_candidate_vecs': opt['fixed_candidate_vecs'],
'rating_frequency': opt['rating_frequency'],
'rating_gap': opt['rating_gap'],
'rating_threshold': opt['rating_threshold'],
'request_feedback': opt['request_feedback'],
'request_rating': opt['request_rating'],
}
# Create model and assign it to the specified task
agent = create_agent(opt, requireModelExists=True)
world = create_task(opt, agent)
if print_parser:
# Show arguments after loading model
print_parser.opt = agent.opt
print_parser.print_args()
# Show some example dialogs:
while True:
world.parley()
if world.epoch_done():
print("EPOCH DONE")
break
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import parlai.core.params as params
from parlai.tasks.self_feeding.build import build
if __name__ == '__main__':
opt = params.ParlaiParser().parse_args(print_args=False)
build(opt)
def download_self_feeding_data(args):
# Download the data from paper "Learning from Dialogue after Deployment: Feed Yourself, Chatbot!"
# https://www.aclweb.org/anthology/P19-1358.pdf
build(vars(args))