Labeled Reports for Multi-Class Text Classification

A collection of text reports for multi-class classification tasks, with expert-generated annotations. It is a monolingual, tabular dataset in CSV format, sourced from the United States region. The dataset size is categorized as 'n1 K', indicating a scale of approximately 1,000 rows.

Use Cases

• Train a text classification model on expert-generated annotations for multi-class prediction tasks.
• Analyze the distribution of labels across the 'n1 K' text reports to identify class balance or imbalance.
• Fine-tune a language model on the tabular text data for region-specific (US) report classification.

Strengths

• Dataset size is categorized as 'n1 K', providing a specific scale for model training.
• Annotations are expert-generated, suggesting a level of label quality.
• Data modality is specified as both 'tabular' and 'text', clarifying its structure.

Limitations

• The exact row count is unknown, only categorized as 'n1 K', limiting precise scalability assessment.
• Specific column names and data fields are not provided, hindering detailed feature analysis.
• The dataset was last updated in October 2022, which may limit its relevance for current applications.

Provenance

Source

https://huggingface.co/datasets/AndresPitta/sg-reports_labeled

Collection Method

machine generated, expert annotated

Freshness

2022-10-25

Geography

us