SleepDepNet: Performance Metrics for Ablation Study on Sleep and Depression

5.5 KB of performance metrics for the SleepDepNet ablation study. The dataset, authored by Akshi Kumar and last updated on 2026-05-07, contains experimental results from a transformer-based multi-task learning framework analyzing Reddit text for sleep quality and depressive sentiment. It includes F1-scores of 0.89 for sleep quality classification and 0.86 for depressive sentiment analysis.

Use Cases

• Benchmarking multi-task learning frameworks for mental health text analysis based on the described F1-scores.
• Evaluating attention mechanisms for interpretability in sleep and depression models.
• Assessing the unified SleepDepScore for combined risk level prioritization in downstream applications.
• Comparing transformer-based models against baseline approaches for sentiment and quality classification.

Strengths

• Performance metrics are quantified with specific F1-scores (0.89 and 0.86).
• Dataset is openly licensed under CC-BY-4.0.
• Update timestamp (2026-05-07) is provided.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB size indicates a very limited scope, likely containing only summary metrics.

Provenance

Source

figshare, author Akshi Kumar.

Collection Method

Results from experiments on the SleepDepNet framework using data from Reddit communities.

Time Range

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Freshness

Last updated 2026-05-07 17:37:16.

Geography

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