How to import a custom data into Prado.

Prerequisites
Please answer the following questions for yourself before submitting an issue.

[yes ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
[yes] I am reporting the issue to the correct repository. (Model Garden official or research directory)
[yes] I checked to make sure that this issue has not been filed already.

1. The entire URL of the file you are using
   https://github.com/tensorflow/models/tree/master/research/sequence_projection

2. Describe the bug
   The model is not learned anything. It just put all the test data into 0.

3. Steps to reproduce
   I tried to run prado model on my own single label dataset. I used tf.data.experimental.make_csv_dataset function to import my csv file. And change the loss function from tf.nn.sparse_softmax_cross_entropy_with_logits to tf.nn.softmax_cross_entropy_with_logits and did not set the max sequence. But after training, the model learns nothing. I compared the data imput between me and the demo(tfds.load), the inside type is slightly different. But I do not know how to construct my csv file into that type. Is there any help you can give?

4. Expected behavior
   It expected to get a well trained model.

5. System information
   OS Platform and Distribution: Linux Ubuntu 16.04
   TensorFlow installed from (source or binary): 2.3
   TensorFlow version (use command below): 2.3
   Python version: 3.6
   Bazel version (if compiling from source): 3.5
   Also I notice that tfds allows me to add a custom dataset into it. But the instruction is too hard to follow. I do not know how to import without download. I hope if I can figure out this, it would help me get the desire structure.