Jetoncount Corpus: Token Statistics for NLP Training

664 tokens per sample on average, according to the provided example. This corpus was used to train the JetonCount model and contains token-level statistics derived from the FineWeb-Edu dataset. It was created by the author 'fromziro' and last updated on June 22, 2026.

Use Cases

• Benchmarking tokenizer efficiency based on provided token, character, and word counts.
• Analyzing text complexity using metrics like average characters per word and punctuation ratio.
• Training or validating tokenization models on educational web text data.

Strengths

• Includes specific per-sample metrics such as character count (2178), word count (335), and token count (664).
• Provides derived linguistic statistics like average characters per word (5.5) and punctuation ratio (0.038).
• Links to a known source dataset (HuggingFaceFW/fineweb-edu) for context.

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 is unknown, which may limit suitability assessment.

Provenance

Source

Derived from the HuggingFaceFW/fineweb-edu dataset.

Collection Method

Processed with a tokenizer (fromziro/Er-Tiny-1.3M) to generate statistics.

Freshness

Last updated 2026-06-22 11:15:15; freshness should be verified.