| --- |
| 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} |
| } |
| ``` |
|
|
