emotion Benchmark (Text Classification)

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Text Classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.

Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others.

In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems.

The archive carries no text for this table; the description above is the archive's text for the task Text Classification. archive 2025-07-28

Results archive 2025-07-28

No rows in the archive for this table at snapshot 2025-07-28. It declares 12 metrics (Accuracy, F1, F1 Macro, F1 Micro, F1 Weighted, Precision Macro, Precision Micro, Precision Weighted, Recall Macro, Recall Micro, Recall Weighted, loss) but no result was ever recorded against it. That says nothing about whether results exist elsewhere.

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