[Fix]: Fix scienceqa

[YuanLiuuuuuu]

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Motivation

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Modification

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Checklist

Before PR:

• Pre-commit or other linting tools are used to fix the potential lint issues.
• Bug fixes are fully covered by unit tests, the case that causes the bug should be added in the unit tests.
• The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
• The documentation has been modified accordingly, like docstring or example tutorials.

After PR:

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• CLA has been signed and all committers have signed the CLA in this PR.

[codecov]

Codecov Report

Patch coverage: 4.76% and project coverage change: -1.07 ⚠️

Comparison is base (f9dcae2) 68.16% compared to head (b87a673) 67.10%.

❗ Current head b87a673 differs from pull request most recent head f26d869. Consider uploading reports for the commit f26d869 to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##              dev    #1581      +/-   ##
==========================================
- Coverage   68.16%   67.10%   -1.07%     
==========================================
  Files         295      299       +4     
  Lines       23372    23762     +390     
  Branches     3713     3769      +56     
==========================================
+ Hits        15932    15945      +13     
- Misses       6880     7255     +375     
- Partials      560      562       +2     

Flag	Coverage Δ
unittests	67.10% <4.76%> (-1.07%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
mmpretrain/datasets/scienceqa.py	0.00% <ø> (ø)
mmpretrain/models/multimodal/__init__.py	40.00% <0.00%> (-4.45%)	⬇️
...retrain/models/multimodal/chinese_clip/__init__.py	0.00% <0.00%> (ø)
mmpretrain/models/multimodal/chinese_clip/bert.py	0.00% <0.00%> (ø)
...ain/models/multimodal/chinese_clip/chinese_clip.py	0.00% <0.00%> (ø)
mmpretrain/models/multimodal/chinese_clip/utils.py	0.00% <0.00%> (ø)
mmpretrain/models/utils/__init__.py	83.33% <0.00%> (ø)
mmpretrain/models/utils/tokenizer.py	0.00% <0.00%> (ø)
mmpretrain/utils/progress.py	72.00% <78.94%> (+8.36%)	⬆️
mmpretrain/apis/base.py	72.44% <100.00%> (+0.28%)	⬆️
... and 2 more

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