Datasets:
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README.md
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Example Usage (Python)](#example-usage-python)
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- [Intended Use]
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- [Dataset Statistics](#dataset-statistics)
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- [Token Statistics](#token-statistics)
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- [License](#license)
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- [Aknowledgements](#aknowledgements)
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- [Citation](#citation)
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- Estimated total tokens: ~1.9B tokens
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- Average number of tokens per document: ~4 120 tokens
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## Quality Signal: CCNet Perplexity
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### Overview
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`CCNet Perplexity` is a linguistic quality indicator used to measure how close a given text is to a high-quality reference corpus, typically Wikipedia.
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It is commonly employed in large-scale dataset filtering pipelines for language model training, in order to identify noisy, malformed, or out-of-domain content.
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The value is exposed in the `quality_signals` field under the key `ccnet_perplexity`.
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### Score Interpretation
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- **Low perplexity (~100–300)**
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Text is linguistically close to Wikipedia:
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⚠️ A high perplexity score does not necessarily imply low semantic value, but rather a **linguistic distance** from the Wikipedia domain.
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### Observations for the Wikisource FR Dataset
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For this dataset:
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- **Median CCNet Perplexity: 298.84**
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url = "https://huggingface.co/datasets/LeMoussel/wikisource_fr"
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}
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```
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Example Usage (Python)](#example-usage-python)
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- [Intended Use][def]
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- [Dataset Statistics](#dataset-statistics)
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- [Token Statistics](#token-statistics)
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- [Quality Signal: CCNet Perplexity](#quality-signal-ccnet-perplexity)
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- [License](#license)
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- [Aknowledgements](#aknowledgements)
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- [Citation](#citation)
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| 131 |
- Estimated total tokens: ~1.9B tokens
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- Average number of tokens per document: ~4 120 tokens
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| 133 |
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| 134 |
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### Quality Signal: CCNet Perplexity
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| 135 |
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`CCNet Perplexity` is a linguistic quality indicator used to measure how close a given text is to a high-quality reference corpus, typically Wikipedia.
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It is commonly employed in large-scale dataset filtering pipelines for language model training, in order to identify noisy, malformed, or out-of-domain content.
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The value is exposed in the `quality_signals` field under the key `ccnet_perplexity`.
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#### Score Interpretation
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- **Low perplexity (~100–300)**
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Text is linguistically close to Wikipedia:
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⚠️ A high perplexity score does not necessarily imply low semantic value, but rather a **linguistic distance** from the Wikipedia domain.
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#### Observations for the Wikisource FR Dataset
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For this dataset:
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- **Median CCNet Perplexity: 298.84**
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url = "https://huggingface.co/datasets/LeMoussel/wikisource_fr"
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}
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```
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[def]: #intended-use
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