How to slice <eos> token with different sentence length about catr

[saahiluppal]

while <pad> in array:
    remove <pad> from array

remove <eos> from array

[rshaojimmy]

Thanks for your quick reply!

But if I remove eos from array, how can model learn to stop generating sentence without encountering the eos token?

[saahiluppal]

Model itself will predict the eos token. If the model doesn't predict eos token, and the entire sentence is gibberish, then the model isn't generalized well or data is insufficient.

…

On Wed, 2 Feb, 2022, 7:38 am Rui Shao, ***@***.***> wrote: Thanks for your quick reply! But if I remove from array, how can model learn to stop generating sentence without encountering the token? — Reply to this email directly, view it on GitHub <#23 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ALJ7DKFWWHLASC3EPZFGGODUZCG2XANCNFSM5NJLJS4Q> . You are receiving this because you commented.Message ID: ***@***.***>

[rshaojimmy]

But we should let trg[:-1] have eos token when we calculate the loss, right?
like this:
trg[:-1] = [x_1, x_2, x_3, eos, pad, pad]
or
trg[:-1] = [x_1, x_2, x_3, eos]

[saahiluppal]

Depends on your training dataset. If your dataset have special tokens like <bos> and <eos>, then yes, these should be considered in loss. While <pad> tokens do not contribute to loss.

…

On Thu, 3 Feb, 2022, 7:47 am Rui Shao, ***@***.***> wrote: But we should let trg[:-1] have eos token when we calculate the loss, right? like this: trg[:-1] = [sos, x_1, x_2, x_3, eos, pad, pad] or trg[:-1] = [sos, x_1, x_2, x_3, eos] — Reply to this email directly, view it on GitHub <#23 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ALJ7DKBJ734ASWNV7LUJFFLUZHQVPANCNFSM5NJLJS4Q> . You are receiving this because you commented.Message ID: ***@***.***>

[rshaojimmy]

Thanks.

In all, I just want to create a dataset with sequences of different lengths. In such a dataset, I insert bos, eos in into the beginning and end of each sequence as the ground-truth. like this:

caps = [sos, x_1, x_2, x_3, eos]

In such a case,

caps[:, :-1] = [sos, x_1, x_2, x_3]
caps[:, 1:] = [x_1, x_2, x_3, eos]

This is what we want for the loss calculation.

outputs = model(samples, caps[:, :-1], cap_masks[:, :-1])
loss = criterion(outputs.permute(0, 2, 1), caps[:, 1:])

However, given different lengths, I have to further insert pad tokens to make them consistent, such as:

caps = [sos, x_1, x_2, x_3, eos, pad, pad, pad]

In such case,

caps[:, :-1] = [sos, x_1, x_2, x_3, eos, pad, pad]
caps[:, 1:] = [x_1, x_2, x_3, eos, pad, pad, pad]

The input of model (caps[:, :-1]) will contain the eos token, which we want to remove.

Considering this, I just further replace the eos token with pad token as pad token will not be calculated for the loss, like this:

caps[:, :-1] = [sos, x_1, x_2, x_3, pad, pad, pad]

And I remain the caps[:, 1:] as

caps[:, 1:] = [x_1, x_2, x_3, eos, pad, pad, pad].

May I ask does this make sense?

[saahiluppal]

you should consider eos token in loss. Because you want your model to learn when to stop generating a sentence.

…

On Thu, 3 Feb, 2022, 1:03 pm Rui Shao, ***@***.***> wrote: Thanks. In all, I just want to create a dataset with sequences of different lengths. In such a dataset, I insert bos, eos in into the beginning and end of each sequence as the ground-truth. like this: caps = [sos, x_1, x_2, x_3, eos] In such a case, caps[:, :-1] = [sos, x_1, x_2, x_3] caps[:, 1:] = [x_1, x_2, x_3, eos] This is what we want for the loss calculation. outputs = model(samples, caps[:, :-1], cap_masks[:, :-1]) loss = criterion(outputs.permute(0, 2, 1), caps[:, 1:]) However, given different lengths, I have to further insert pad tokens to make them consistent, such as: caps = [sos, x_1, x_2, x_3, eos, pad, pad, pad] In such case, caps[:, :-1] = [sos, x_1, x_2, x_3, eos, pad, pad] caps[:, 1:] = [x_1, x_2, x_3, eos, pad, pad, pad] The input of model (caps[:, :-1]) will contain the eos token, which we want to remove. Considering this, I just further replace the eos token with pad token as pad token will not be calculated for the loss, like this: caps[:, :-1] = [sos, x_1, x_2, x_3, pad, pad, pad] And I remain the caps[:, 1:] as caps[:, 1:] = [x_1, x_2, x_3, eos, pad, pad, pad]. May I ask does this make sense? — Reply to this email directly, view it on GitHub <#23 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ALJ7DKAEX266V3MZ7V4YBDTUZIVTTANCNFSM5NJLJS4Q> . You are receiving this because you commented.Message ID: ***@***.***>