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Datasets:
epfl-dlab
/
zip2zip-wikitext-repeat-phi35

Tasks:
Text Generation
Modalities:
Tabular
Text
Formats:
json
Languages:
English
Size:
1K - 10K
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zip2zip-wikitext-repeat-phi35
File size: 3,875 Bytes
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---
license: cc-by-sa-3.0
language:
  - en
task_categories:
  - text-generation
pretty_name: Zip2Zip Repeated WikiText Stress Tests (Phi-3.5)
size_categories:
  - 1K<n<10K
configs:
  - config_name: repeat4
    data_files:
      - split: test
        path: repeat4/test.jsonl
  - config_name: repeat8
    data_files:
      - split: test
        path: repeat8/test.jsonl
---

# Zip2Zip Repeated WikiText Stress Tests (Phi-3.5)

This repository contains two controlled evaluation corpora for studying
merge-size transfer in Zip2Zip models. They are derived from the document-level
WikiText-2 raw test split and built specifically with the
`microsoft/Phi-3.5-mini-instruct` tokenizer.

## Configurations

| Config | Repetitions per source block | Rows | Repeated base tokens | SHA-256 of `test.jsonl` |
|---|---:|---:|---:|---|
| `repeat4` | 4 | 1,329 | 1,297,250 | `82fce0f7667d48f83f627ebc14b8148c64311127f38bc66741986135512a4c74` |
| `repeat8` | 8 | 2,651 | 2,617,485 | `48aa4185c6c961763ebea17ea8e46bb41eb8fd63300384643dd0ab2af2d3b049` |

Load either configuration with Hugging Face Datasets:

```python
from datasets import load_dataset

repeat4 = load_dataset(
    "epfl-dlab/zip2zip-wikitext-repeat-phi35",
    "repeat4",
    split="test",
)
repeat8 = load_dataset(
    "epfl-dlab/zip2zip-wikitext-repeat-phi35",
    "repeat8",
    split="test",
)
```

## Construction

We start from `EleutherAI/wikitext_document_level`, configuration
`wikitext-2-raw-v1`, split `test`, and apply the standard WikiText-2
detokenization used by `lm-evaluation-harness`. Each document is tokenized with
Phi-3.5, divided into token-aware source blocks, decoded, and repeated either
four or eight times with blank-line separators. Blocks are shortened as needed
so that every resulting row contains at most 1,000 Phi-3.5 base tokens and fits
within a single 1,024-token evaluation window. Every repeated copy is included
in the language-modeling loss.

The JSONL rows contain the repeated `text`, UTF-8 byte and whitespace-delimited
word denominators, source document and block identifiers, and token-count
metadata. The accompanying manifests record aggregate construction statistics.

## Intended use

Repeat-4 and Repeat-8 are opt-in stress tests for hierarchical hyper-token
merge-size transfer. Repetition increases the frequency of long LZW dictionary
entries, making the difference between maximum merge sizes 3 and 4 measurable.
They are not general-purpose language-modeling benchmarks. Later repeated
copies are intentionally easier to predict, so absolute perplexities must not
be compared across standard WikiText, Repeat-4, and Repeat-8. Model comparisons
should be made within the same configuration and evaluation segmentation.

Because row boundaries are chosen using Phi-3.5 tokenization, these artifacts
should not be silently reused with another tokenizer. Supporting another
tokenizer requires rebuilding and publishing separately identified artifacts.

## Reproducibility

The source code used to construct the corpora is
`scripts/build_repeated_wikitext.py` in the Zip2Zip repository. The checked-in
Zip2Zip lm-eval tasks are named `zip2zip_wikitext_repeat4` and
`zip2zip_wikitext_repeat8`.

## Source and license

The source corpus is the WikiText-2 raw test split from
[`EleutherAI/wikitext_document_level`](https://huggingface.co/datasets/EleutherAI/wikitext_document_level),
which is distributed under the Creative Commons Attribution-ShareAlike 3.0
license. These transformed evaluation artifacts are released under the same
license. Users should also cite the original WikiText paper:

```bibtex
@inproceedings{merity2017pointer,
  title     = {Pointer Sentinel Mixture Models},
  author    = {Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
  booktitle = {International Conference on Learning Representations},
  year      = {2017}
}
```