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SST-5

Introduced by Richard Socher et al. in Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank1 Oct 2013 archive 2025-07-28

The SST-5, also known as the Stanford Sentiment Treebank with 5 labels, is a dataset used for sentiment analysis. The SST-5 dataset consists of 11,855 single sentences extracted from movie reviews¹. It includes a total of 215,154 unique phrases from parse trees, each annotated by 3 human judges¹. Each phrase is labeled as either negative, somewhat negative, neutral, somewhat positive, or positive. This is why it's referred to as SST-5 or SST fine-grained.

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

Papers archive 2025-07-28

2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 338. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations 1 1 21 Sep 2023 not harvested
OCD: Learning to Overfit with Conditional Diffusion Models 1 1 2 Oct 2022 ran 0 of 2 samples (2 unverified)

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • SST-5 Fine-grained classification
  • SST-5

2 variant names, as the archive lists them.

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