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编写于
11月 05, 2018
作者:
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Abhishek Rao
提交者:
GitHub
11月 05, 2018
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@@ -306,6 +306,27 @@ use BERT for any single-sentence or sentence-pair classification task.
...
@@ -306,6 +306,27 @@ use BERT for any single-sentence or sentence-pair classification task.
Note: You might see a message
`Running train on CPU`
. This really just means
Note: You might see a message
`Running train on CPU`
. This really just means
that it's running on something other than a Cloud TPU, which includes a GPU.
that it's running on something other than a Cloud TPU, which includes a GPU.
#### Prediction from classifier
Once you have trained your classifier you can use it in inference mode by using the --do_predict=true command.
You need to have a file named predict.tsv in the input folder.
Output will be created in file called predict_results.tsv in the output folder.
Each line will contain output for each sample, columns are the class probabilities.
```
shell
export
BERT_BASE_DIR
=
/path/to/bert/uncased_L-12_H-768_A-12
export
GLUE_DIR
=
/path/to/glue
export
TRAINED_CLASSIFIER
=
/path/to/fine/tuned/classifier
python run_classifier.py
\
--task_name
=
MRPC
\
--do_predict
=
true
\
--data_dir
=
$GLUE_DIR
/MRPC
\
--vocab_file
=
$BERT_BASE_DIR
/vocab.txt
\
--bert_config_file
=
$BERT_BASE_DIR
/bert_config.json
\
--init_checkpoint
=
$TRAINED_CLASSIFIER
\
--max_seq_length
=
128
\
--output_dir
=
/tmp/mrpc_output/
```
### SQuAD
### SQuAD
The Stanford Question Answering Dataset (SQuAD) is a popular question answering
The Stanford Question Answering Dataset (SQuAD) is a popular question answering
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