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config_data.py
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config_data.py
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tfrecord_data_dir = "data/MRPC"
max_seq_length = 128
num_classes = 2
num_train_data = 3668
train_batch_size = 32
max_train_epoch = 3
display_steps = 50 # Print training loss every display_steps; -1 to disable
eval_steps = -1 # Eval on the dev set every eval_steps; -1 to disable
# Proportion of training to perform linear learning
# rate warmup for. E.g., 0.1 = 10% of training.
warmup_proportion = 0.1
eval_batch_size = 8
test_batch_size = 8
feature_original_types = {
# Reading features from TFRecord data file.
# E.g., Reading feature "input_ids" as dtype `tf.int64`;
# "FixedLenFeature" indicates its length is fixed for all data instances;
# and the sequence length is limited by `max_seq_length`.
"input_ids": ["tf.int64", "FixedLenFeature", max_seq_length],
"input_mask": ["tf.int64", "FixedLenFeature", max_seq_length],
"segment_ids": ["tf.int64", "FixedLenFeature", max_seq_length],
"label_ids": ["tf.int64", "FixedLenFeature"]
}
feature_convert_types = {
# Converting feature dtype after reading. E.g.,
# Converting the dtype of feature "input_ids" from `tf.int64` (as above)
# to `tf.int32`
"input_ids": "tf.int32",
"input_mask": "tf.int32",
"label_ids": "tf.int32",
"segment_ids": "tf.int32"
}
train_hparam = {
"allow_smaller_final_batch": False,
"batch_size": train_batch_size,
"dataset": {
"data_name": "data",
"feature_convert_types": feature_convert_types,
"feature_original_types": feature_original_types,
"files": "{}/train.tf_record".format(tfrecord_data_dir)
},
"shuffle": True,
"shuffle_buffer_size": 100
}
eval_hparam = {
"allow_smaller_final_batch": True,
"batch_size": eval_batch_size,
"dataset": {
"data_name": "data",
"feature_convert_types": feature_convert_types,
"feature_original_types": feature_original_types,
"files": "{}/eval.tf_record".format(tfrecord_data_dir)
},
"shuffle": False
}
test_hparam = {
"allow_smaller_final_batch": True,
"batch_size": test_batch_size,
"dataset": {
"data_name": "data",
"feature_convert_types": feature_convert_types,
"feature_original_types": feature_original_types,
"files": "{}/predict.tf_record".format(tfrecord_data_dir)
},
"shuffle": False
}