Handle label imbalance in binary classification tasks on text benchmark

[vid-koci]

Labels in the text benchmarks are imbalanced and weighting the positive labels improves performance.
Experiments done on fake dataset (5% positive labels) with text_embedded and RoBERTa encodings:

• ResNet result changes 91.1% -> 93.4%
• FTTransformer result remains unchanged
• Trompt result changes 95.2% -> 95.8%

The differences were even more stark with distilled roberta, but we aren't reporting those anywhere so I didn't note them down.

More results are pending

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 93.52%. Comparing base (893678f) to head (3801ffd).

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #376   +/-   ##
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  Coverage   93.52%   93.52%           
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  Files         124      124           
  Lines        6451     6451           
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  Hits         6033     6033           
  Misses        418      418           

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[weihua916]

Thanks!