SleepDepScore Evaluation Metrics for Sleep and Depression NLP Models

Evaluation metrics for the SleepDepNet model, a transformer-based multi-task learning framework for analyzing sleep quality and depressive sentiment from Reddit text. The dataset, authored by Akshi Kumar and last updated on 2026-05-07, contains performance scores including F1-scores of 0.89 for sleep quality classification and 0.86 for depressive sentiment analysis. It is stored in an XLS file with a size of 5.5 KB.

Use Cases

• Benchmarking NLP models for sleep quality classification based on the reported F1-score of 0.89
• Evaluating sentiment analysis models for depressive language based on the reported F1-score of 0.86
• Validating multi-task learning frameworks for joint modelling of sleep and mental health from online discourse
• Assessing the unified SleepDepScore metric for combined risk level prioritization in downstream applications

Strengths

• Includes specific, high-performance evaluation metrics (F1-scores of 0.89 and 0.86)
• Dataset is openly licensed under CC-BY-4.0
• Model framework described provides interpretability through attention mechanisms

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is very small (5.5 KB), indicating limited scope

Provenance

Source

Akshi Kumar via figshare

Collection Method

Derived from experimental results of the SleepDepNet model applied to Reddit data.

Time Range

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Freshness

Last updated 2026-05-07 17:37:18; freshness should be verified

Geography

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