GOT_Uncensored: A Merged Dataset for Uncensored Reasoning Fine-Tuning

GOT_Uncensored is a curated dataset designed for fine-tuning language models on uncensored reasoning tasks. The dataset was created by author '11-47' and was last updated on May 31, 2026. Its description indicates it is a high-quality merged collection from multiple sources.

Use Cases

• Fine-tuning language models for uncensored reasoning based on the dataset's stated purpose.
• Evaluating model performance on complex, unrestricted problem-solving tasks.
• Training models to generate detailed, unfiltered logical explanations.
• Benchmarking the reasoning capabilities of large language models.

Strengths

• The dataset is described as 'high-quality' and 'curated' in the provided overview.
• It is a merged collection from multiple source datasets, suggesting breadth of content.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

huggingface, author 11-47

Collection Method

Merged from multiple source datasets, as indicated in the description.

Time Range

null

Freshness

Last updated 2026-05-31 08:38:38; freshness should be verified.

Geography

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