---
license: apache-2.0
language:
- en
task_categories:
- text-generation
tags:
- pretraining-data
- data-curation
- web-scraping
size_categories:
- 10M<n<100M
---

# ReScraper-Data

Data released with ReScraper, a 0.6B refiner that replaces the heuristic HTML-to-text stack of a
pretraining data pipeline with a single small language model.

- **Model:** [`cx-cmu/ReScraper`](https://huggingface.co/cx-cmu/ReScraper)
- **Code:** [`cxcscmu/ReScraper`](https://github.com/cxcscmu/ReScraper)

All four subsets come from the same source pool: the 10% raw-HTML sample of the DCLM pool
(`dclm-pool-400m-1x-html-jsonl-step3a-10pct`), 18,025,558 pages over 10,319 shards.

## Contents

- [Subsets](#subsets)
- [Schema](#schema)
- [Loading](#loading)
- [License](#license)

## Subsets

| Path | Rows | Size | What it is |
|---|---:|---:|---|
| `data/ReScraper-Corpus/` | 10,056,105 | 12.4 GB | The curated pretraining corpus: model output after the post-filter and Bloom-filter deduplication. 7.44B GPT-NeoX-20B tokens. |
| `data/ReScraper-Pool-Output/` | 10,646,236 | 13.3 GB | The refiner's raw output over the whole pool, before deduplication. Use this to study the model's behaviour page by page. |
| `data/ReScraper-SFT-Stage1/` | 1,383,115 | 3.9 GB | Stage 1 training set: extraction plus the operation decision. |
| `data/ReScraper-SFT-Stage2/` | 131,484 | 0.4 GB | Stage 2 training set, which upweights `<rewrite>` so the model learns that operation. |

Files are Parquet, split into roughly 1 GB parts named
`<subset>-en-part-XXXX-of-YYYY.parquet`.

## Schema

**Corpus and pool output**

| Field | Type | Meaning |
|---|---|---|
| `text` | string | the cleaned page |
| `operation` | string | the operation the model chose: `<keep>`, `<edit>` or `<rewrite>` (deleted pages are not materialized) |
| `bff_contained_ngram_count_before_dedupe` | int | deduplication bookkeeping (corpus only) |

**SFT sets**

| Field | Type | Meaning |
|---|---|---|
| `input` | string | the rendered page, one block per line with `<lid:n>` line ids |
| `output` | string | the target program: an `<extract>` block of line removals followed by the operation tag |

## Loading

```python
from datasets import load_dataset

corpus = load_dataset("cx-cmu/ReScraper-Data", data_dir="data/ReScraper-Corpus", split="train")
sft    = load_dataset("cx-cmu/ReScraper-Data", data_dir="data/ReScraper-SFT-Stage1", split="train")
```

To reproduce pretraining, tokenize the corpus with the GPT-NeoX-20B tokenizer; the code
repository has the tokenization and pretraining scripts.

## License

Apache 2.0. The underlying pages come from Common Crawl via the DCLM pool and keep their
original terms.