Clarification on Padding Process in BERT Model Construction

Hi there,

In my endeavor to construct a BERT model from the ground up for the purpose of gaining hands-on experience and a comprehensive understanding of the model, I have encountered a point of confusion regarding the padding process.

According to my interpretation of the `dataset.py` file, the standard procedure involves concatenating two sentences and adding pad tokens at the end of the combined sequence, similar to typical padding practices in other tasks. Here's an example:

```
Sent_A= The cat is walking (Len is 4)
Sent_B= The dog is barking at the tree (Len is 7)

Maximum sequence length: 15

Sequence: The cat is walking The dog is barking at the tree [PAD] [PAD] [PAD] [PAD]
Segment: 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0
```

However, in alternative implementations [1](https://neptune.ai/blog/how-to-code-bert-using-pytorch-tutorial) [2](https://coaxsoft.com/blog/building-bert-with-pytorch-from-scratch), I've observed padding aimed at equalizing the lengths of the two sentences, resulting in a structure like this:

```
Sent_A= The cat is walking (Len is 4)
Sent_B= The dog is barking at the tree (Len is 7)

Maximum sequence length: 15

Sequence: The cat is walking [PAD] [PAD] [PAD] The dog is barking at the tree [PAD]
Segment: 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0
```

This discrepancy has left me in a state of uncertainty—whether my understanding from the `dataset.py` file is accurate, or if the alternative implementations I've come across have a different rationale behind their padding approach. Could you kindly provide clarification on this matter?