I’m using tfds to create a custom text dataset for classification with the BigBird NLP package. I’m receiving an error message “normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization” and I’m unable to find any information on how to resolve this error. I have included the log file below. BigBird completes the classification but the accuracy is 0.0 since no data from the custom tfds dataset appears to be flowing to BigBird classification in the correct format.
My GPUS are 0 (0 is the first GPU so one is assigned)
INFO[build.py]: Loading dataset from path: /mmfs1/home/0156fieldsj/tensorflow_datasets/my_dataset/my_dataset.py
INFO[dataset_info.py]: Load dataset info from /mmfs1/home/0156fieldsj/tensorflow_datasets/my_dataset/1.0.0
INFO[build.py]: download_and_prepare for dataset my_dataset/1.0.0…
INFO[dataset_builder.py]: Reusing dataset my_dataset (/mmfs1/home/0156fieldsj/tensorflow_datasets/my_dataset/1.0.0)
INFO[build.py]: Dataset generation complete…
tfds.core.DatasetInfo(
name=‘my_dataset’,
full_name=‘my_dataset/1.0.0’,
description=“”"
Description is formatted as markdown.
It should also contain any processing which has been applied (if any),
(e.g. corrupted example skipped, images cropped,...):
""",
homepage='https://www.tensorflow.org/datasets/catalog/my_dataset',
data_path='/mmfs1/home/0156fieldsj/tensorflow_datasets/my_dataset/1.0.0',
download_size=Unknown size,
dataset_size=379.77 KiB,
features=FeaturesDict({
'essay': Text(shape=(), dtype=tf.string),
'status': tf.int32,
}),
supervised_keys=None,
disable_shuffling=False,
splits={
'test': <SplitInfo num_examples=30, num_shards=1>,
'train': <SplitInfo num_examples=69, num_shards=1>,
},
citation="""""",
)
normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.
0%| | 0/199 [00:00<?, ?it/s]
42%|████▏ | 84/199 [00:00<00:00, 832.21it/s]
100%|██████████| 199/199 [00:00<00:00, 1123.67it/s]
0%| | 0/2000 [00:00<?, ?it/s]
0%| | 0/2000 [00:00<?, ?it/s]
0it [00:00, ?it/s]
0it [00:00, ?it/s]
Loss = 0.0 Accuracy = 0.0