def main():
parser = setup_args()
parser.set_defaults(
model='projects.personachat.kvmemnn.kvmemnn:Kvmemnn',
model_file='models:convai2/kvmemnn/model',
numthreads=80,
)
opt = parser.parse_args(print_args=False)
# build all profile memory models
fnames = ['kvmemnn.tgz']
opt['model_type'] = 'kvmemnn' # for builder
download_models(opt, fnames, 'convai2')
return eval_f1(parser, print_parser=parser)
def main():
parser = setup_args()
parser.set_params(
model='language_model',
model_file='models:convai2/language_model/model',
dict_file='models:convai2/language_model/model.dict',
batchsize=20,
)
opt = parser.parse_args()
opt['model_type'] = 'language_model'
fnames = ['model', 'model.dict', 'model.opt']
download_models(opt,
fnames,
'convai2',
version='v2.0',
use_model_type=True)
return eval_f1(opt, print_parser=parser)
def main():
parser = setup_args()
parser.set_params(
model='legacy:seq2seq:0',
model_file='models:convai2/seq2seq/convai2_self_seq2seq_model',
dict_file='models:convai2/seq2seq/convai2_self_seq2seq_model.dict',
dict_lower=True,
batchsize=32,
)
opt = parser.parse_args(print_args=False)
if (opt.get('model_file',
'').find('convai2/seq2seq/convai2_self_seq2seq_model') != -1):
opt['model_type'] = 'seq2seq'
fnames = [
'convai2_self_seq2seq_model.tgz',
'convai2_self_seq2seq_model.dict', 'convai2_self_seq2seq_model.opt'
]
download_models(opt, fnames, 'convai2', version='v3.0')
return eval_f1(opt, print_parser=parser)
#!/usr/bin/env python3
# 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.
"""Evaluate pre-trained model trained for f1 metric
Key-Value Memory Net model trained on convai2:self
"""
from parlai.core.build_data import download_models
from projects.convai2.eval_f1 import setup_args, eval_f1
if __name__ == '__main__':
parser = setup_args()
parser.set_defaults(
model='projects.personachat.kvmemnn.kvmemnn:Kvmemnn',
model_file='models:convai2/kvmemnn/model',
numthreads=80,
)
opt = parser.parse_args(print_args=False)
# build all profile memory models
fnames = ['kvmemnn.tgz']
opt['model_type'] = 'kvmemnn' # for builder
download_models(opt, fnames, 'convai2')
eval_f1(parser, print_parser=parser)
