Add T5 model #916
Merged
Add T5 model #916
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
|
@yingyibiao @JunnYu 这个PR还有效吗？ |
有效 |
|
需要在example/language_model下面新增一个案例 |
yingyibiao
reviewed
Nov 11, 2021
|
权重已上传～ |
ZeyuChen
reviewed
Nov 15, 2021
yingyibiao
reviewed
Nov 17, 2021
yingyibiao
reviewed
Nov 19, 2021
Comment on lines
1623
to
1626
| super().__init__() | ||
| self.t5 = t5 | ||
| del self.t5.decoder | ||
| paddle.device.cuda.empty_cache() |
There was a problem hiding this comment.
T5EncoderModel还是参考hf的实现来写；目前这种写法会存在一个显存占用的脉冲，容易造成oom。
There was a problem hiding this comment.
这个类建议按照参考T5Model的方式来初始化（传入构造T5EncoderModel的具体参数）
yingyibiao
reviewed
Nov 22, 2021
| 'T5Model', | ||
| "T5PretrainedModel", | ||
| 'T5ForConditionalGeneration', | ||
| 'T5EncoderModel', |
yingyibiao
approved these changes
Nov 22, 2021
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Outdated suggestions cannot be applied.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.
飞桨论文复现挑战赛（第四期）Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer 论文复现提交。