I've tried to finetune the model on my own text summarization dataset. Before doing that, I tested using tfrecord as the input file. So I put /tmp/bigb/tfds/aeslc/1.0.0 as data_dir:
flags.DEFINE_string(
"data_dir", "/tmp/bigb/tfds/aeslc/1.0.0",
"The input data dir. Should contain the TFRecord files. "
"Can be TF Dataset with prefix tfds://")
Then I run run_summarization.py. But I got the following error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Feature: document (data type: string) is required but could not be found.
[[{{node ParseSingleExample/ParseExample/ParseExampleV2}}]]
[[MultiDeviceIteratorGetNextFromShard]]
[[RemoteCall]]
[[IteratorGetNext]]
[[Mean/_19475]]
(1) Invalid argument: Feature: document (data type: string) is required but could not be found.
[[{{node ParseSingleExample/ParseExample/ParseExampleV2}}]]
[[MultiDeviceIteratorGetNextFromShard]]
[[RemoteCall]]
[[IteratorGetNext]]
Could anyone advise me how to finetune the model using tfrecord as the input file?
I've tried to finetune the model on my own text summarization dataset. Before doing that, I tested using tfrecord as the input file. So I put
/tmp/bigb/tfds/aeslc/1.0.0
asdata_dir
:Then I run run_summarization.py. But I got the following error:
Could anyone advise me how to finetune the model using tfrecord as the input file?