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doc-doc / NExT-QA

NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (CVPR'21)
MIT License
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Can you provide the BERT features of the candidate answers? #4

Open zhangxi1997 opened 3 years ago

zhangxi1997 commented 3 years ago

Hi, thanks for your sharing. I wonder can you provide the pre-trained BERT features of the candidate answers? Thanks a lot!

doc-doc commented 3 years ago

Thanks for your interest. As we finetune BERT for multi-choice QA, each answer is appended to the question as a global sentence. If you want the separated candidate feature, you can split the provided global-sentence feature according to the format (1): [CLS] question [SEP] option_0 [SEP], and the token representations behind the first '[SEP]' belong to the candidate answer. You need to use BERT's buildin tokenizer to get the tokenized represetantion of the sentence (1) and find the index of ['SEP'].

zhangxi1997 commented 3 years ago

Thanks for your quick reply. And I wonder can you provide the finetune BERT to help me extract the candidate answer features?

doc-doc commented 3 years ago

Hi, currently, we are not going to release this part of code, but you can refer to this file for details.

junwenchen commented 1 year ago

Hi,

Thanks for the explanation. Can you let me know what loss is used for fine-tuning bert?

doc-doc commented 1 year ago

It's softmax cross-entropy.

doc-doc commented 1 year ago

Hi, please find the edited code for fintuning BERT on NExT-QA here. You can also fine-tune other datasets by using the code.

junwenchen commented 1 year ago

Thanks a lot