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ERNIE
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6ed030f7
E
ERNIE
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6ed030f7
编写于
9月 04, 2019
作者:
K
kirayummy
提交者:
GitHub
9月 04, 2019
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差异文件
Merge pull request #313 from Meiyim/dev
default `--predict_batch_size=8`
上级
e6e0af89
58066a1e
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
1 addition
and
3 deletion
+1
-3
finetune_args.py
finetune_args.py
+1
-1
run_classifier.py
run_classifier.py
+0
-2
未找到文件。
finetune_args.py
浏览文件 @
6ed030f7
...
@@ -78,7 +78,7 @@ data_g.add_arg("dev_set", str, None, "Path to validation data.")
...
@@ -78,7 +78,7 @@ data_g.add_arg("dev_set", str, None, "Path to validation data.")
data_g
.
add_arg
(
"vocab_path"
,
str
,
None
,
"Vocabulary path."
)
data_g
.
add_arg
(
"vocab_path"
,
str
,
None
,
"Vocabulary path."
)
data_g
.
add_arg
(
"max_seq_len"
,
int
,
512
,
"Number of words of the longest seqence."
)
data_g
.
add_arg
(
"max_seq_len"
,
int
,
512
,
"Number of words of the longest seqence."
)
data_g
.
add_arg
(
"batch_size"
,
int
,
32
,
"Total examples' number in batch for training. see also --in_tokens."
)
data_g
.
add_arg
(
"batch_size"
,
int
,
32
,
"Total examples' number in batch for training. see also --in_tokens."
)
data_g
.
add_arg
(
"predict_batch_size"
,
int
,
None
,
"Total examples' number in batch for predict. see also --in_tokens."
)
data_g
.
add_arg
(
"predict_batch_size"
,
int
,
8
,
"Total examples' number in batch for predict. see also --in_tokens."
)
data_g
.
add_arg
(
"in_tokens"
,
bool
,
False
,
data_g
.
add_arg
(
"in_tokens"
,
bool
,
False
,
"If set, the batch size will be the maximum number of tokens in one batch. "
"If set, the batch size will be the maximum number of tokens in one batch. "
"Otherwise, it will be the maximum number of examples in one batch."
)
"Otherwise, it will be the maximum number of examples in one batch."
)
...
...
run_classifier.py
浏览文件 @
6ed030f7
...
@@ -80,8 +80,6 @@ def main(args):
...
@@ -80,8 +80,6 @@ def main(args):
if
args
.
random_seed
is
not
None
:
if
args
.
random_seed
is
not
None
:
startup_prog
.
random_seed
=
args
.
random_seed
startup_prog
.
random_seed
=
args
.
random_seed
if
args
.
predict_batch_size
==
None
:
args
.
predict_batch_size
=
args
.
batch_size
if
args
.
do_train
:
if
args
.
do_train
:
train_data_generator
=
reader
.
data_generator
(
train_data_generator
=
reader
.
data_generator
(
input_file
=
args
.
train_set
,
input_file
=
args
.
train_set
,
...
...
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