---
license: mit
pretty_name: BART Dataset v2
language:
- en
size_categories:
- 100K<n<1M
tags:
- books
- historical
- pre-1930
- ocr
- institutional-books
- pretraining
- deboilerplated
dataset_info:
  features:
  - name: text
    dtype: string
---

# BART Dataset v2

The second version of the BART pretraining corpus, focused on stripping low-quality text —
boilerplate and OCR corruption — out of
[v1](https://huggingface.co/datasets/jbduran/bart-dataset-v1).

| | |
|---|---|
| **Documents** | 149,745 (93.44% of v1) |
| **Characters** | 106,274,384,672 (89.50% of v1) |
| **Tokens** | ~24B (estimated) |
| **Shards** | 473 (one per v1 shard, same basename) |
| **Source** | [BART Dataset v1](https://huggingface.co/datasets/jbduran/bart-dataset-v1) |
| **Schema** | single string column `text` |

**Lineage** — three cumulative filtering stages over the same corpus:

[Institutional Books 1.0](https://huggingface.co/datasets/institutional/institutional-books-hl) →
[v1](https://huggingface.co/datasets/jbduran/bart-dataset-v1) →
**v2** →
[v3](https://huggingface.co/datasets/jbduran/bart-dataset-v3)

## How it was made

This phase of filtering targeted boilerplate text and OCR corruption that snuck through
Institutional Books' own filters. It removed useless headers, footers and library stamps, alongside
gibberish symbols and antiquated characters, through heuristic regex filters.

We also followed **Michael Hla's log-prior filter** (from Machina Mirabilis / gpt1900), which
estimates a text sequence's unconditional log-prior probability by summing token-level log
probabilities, then trims improbable or out-of-distribution tokens. Documents were kept inside a
moderate GPT-2 token log-prior band of **p2.5–p97.5**.

Whole books are preserved as rows, and each source shard maps to one output shard of the same
basename — so v1's validation shard, `shard_00472.parquet`, survives as v2's.

The post-1900 physics keyword filter from Hla's original approach was **deliberately skipped**,
because this corpus intentionally keeps texts up to the 1930s.

### A note on the result

We expected a major improvement in validation bits-per-byte. Our models actually saw a **decline in
performance** after this phase — because removing the boilerplate removed most of the easy items
for a model to replicate. The repetitive headers, footers and stamps were cheap tokens to predict,
and stripping them made the remaining corpus genuinely harder. This is worth knowing before you
choose v2 over v1: the text is cleaner, but the benchmark number moves the wrong way.

## Filtering summary

| Stage | Rule |
|---|---|
| Structural | printable character ratio `>= 0.85`; `<= 50` OCR artifacts; `>= 500` chars raw and clean |
| Log-prior | mean GPT-2 token log-prior within p2.5–p97.5, i.e. `-11.039` to `-9.832` |
| Granularity | whole books kept as rows — no chunking |
| Skipped | post-1900 physics keyword filter (incompatible with a 1930s cutoff) |

| Removal reason | Documents |
|---|---:|
| `prior_high` | 3,991 |
| `prior_low` | 3,838 |
| `ocr_artifacts` | 2,689 |
| **Total removed** | **10,518 (6.56%)** |

<details>
<summary><b>Log-prior threshold calibration</b></summary>

Thresholds were calibrated on a sample of 3,946 raw documents, of which 3,890 survived structural
filtering (56 removed as `ocr_artifacts`). Estimated prior-based removal from that sample: 5.04%.

| Percentile | Mean log-prior |
|---:|---:|
| 1 | -11.2520 |
| **2.5** (low cut) | **-11.0390** |
| 5 | -10.8738 |
| 10 | -10.7243 |
| 25 | -10.5201 |
| 50 | -10.3274 |
| 75 | -10.1492 |
| 90 | -9.9888 |
| 95 | -9.9095 |
| **97.5** (high cut) | **-9.8320** |
| 99 | -9.7501 |

Full values in `_prior/thresholds.json`; per-run detail in `cleaning_report.json`.

</details>

<details>
<summary><b>Character accounting</b></summary>

| Stage | Characters |
|---|---:|
| Raw (v1) | 118,745,375,871 |
| After structural clean, before prior filter | 114,051,142,521 |
| Kept | 106,274,384,672 |
| **Kept vs. raw** | **89.50%** |

</details>

## Schema and usage

Single string column named `text`.

```python
from datasets import load_dataset

ds = load_dataset("jbduran/bart-dataset-v2", split="train", streaming=True)
print(next(iter(ds))["text"][:500])
```

Artifacts: `_prior/` (log-prior thresholds and sample stats), `cleaning_report.json` (full run
report).

## Citation

Ultimately derived from Institutional Books 1.0:

```bibtex
@misc{institutionalbooks2025,
  title  = {Institutional Books 1.0: A 242B Token Dataset from Harvard Library's
            Collections, Refined for Accuracy and Usability},
  year   = {2025},
  eprint = {2506.08300},
  archivePrefix = {arXiv}
}
```