---
license: apache-2.0
task_categories:
- feature-extraction
- tabular-regression
language:
- en
pretty_name: JetonCount's Corpus
tags:
- tabular
- regression
- statistics
size_categories:
- 10M<n<100M
---

# JetonCount's Corpus

This is the corpus used to train JetonCount.

## JSONL Format

```json
{
  "source_file": "token_stats\\HuggingFaceFW_fineweb-edu00000.jsonl",
  "dataset_dir": "HuggingFaceFW/fineweb-edu",
  "index": 4020,
  "chars": 2178,
  "words": 335,
  "avg_chars_per_word": 5.504478,
  "longest_word_chars": 33,
  "punctuation_ratio": 0.037649,
  "symbol_ratio": 0.00551,
  "tokens": 664,
  "vocab_size": 2560,
  "tokenizer_dir": "fromziro/Er-Tiny-1.3M"
}
```

## Field Descriptions

1. `source_file`:
   The original file used to generate the statistics on our local system.
2. `dataset_dir`:
   The Hugging Face dataset identifier from which the sample originated.
3. `index`:
   The index of the sample within the dataset when iterating through it using the Hugging Face datasets library.
4. `chars`:
   The total number of characters in the text sample.
5. `words`:
   The total number of words in the text sample.
6. `avg_chars_per_word`:
    The average number of characters per word in the text sample.
8. `longest_word_chars`:
   The length, in characters, of the longest word in the text sample.
9. `punctuation_ratio`:
   The fraction of characters in the text sample that are punctuation characters.
10. `symbol_ratio`:
   The fraction of characters in the text sample that are symbols (such as `@`, `#`, `$`, `%`, `&`, etc.).
11. `tokens`:
    The number of tokens produced by the tokenizer specified in `tokenizer_dir`.
12. `vocab_size`:
    The vocabulary size of the tokenizer used to generate the token count. (Ranges from 250 to 256,000)
13. `tokenizer_dir`:
    The Hugging Face repository containing the tokenizer used to generate the token count.

## Use Cases
1. Training ML models: Train token count predictors, tabular models, neural networks, gradient boosting models, or anything else you want.
2. Tokenizer research: Study how text characteristics such as length, vocabulary size, and symbol density affect tokenization behavior.
3. Benchmarking tokenizers: Compare tokenization efficiency across different tokenizers and vocabulary sizes using a large and diverse collection of text samples.
4. Feature engineering research: Explore which textual features are most predictive of token counts and develop new feature sets for token estimation tasks.
5. Dataset analysis: Analyze the statistical properties of large text corpora.
6. Educational purposes: Learn about tokenization, text statistics, feature engineering, and regression.
7. Anything else: The dataset is released to the community to be used in any way.