[Feature] Filter negative labels #244
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Co-authored-by: Zhihao Lin <36994684+LZHgrla@users.noreply.github.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
在数据集处理时，如果训练集中
input过长，超过了max_length，这种数据虽然在tokenizer部分被标记成了-100，但仍然会参与向前计算和损失函数的计算，这会浪费计算资源，因此写了一个filter_out_all_negative_labels函数来过滤掉负标签的样本。这样使得负样本不会参与向前传播的计算和损失函数的计算。During dataset processing, if the
inputin the training set is too long and exceedsmax_length, such data, although marked as-100in the tokenizer section, will still participate in the forward computation and loss function calculation, which can waste computational resources. Therefore, a function namedfilter_out_all_negative_labelswas written to filter out samples with negative labels. This ensures that samples with negative labels do not participate in forward propagation and loss function calculations.