---
language:
- multilingual
license: apache-2.0
task_categories:
- other
size_categories:
- n<1K
tags:
- tokenization
- testing
- swift
configs:
- config_name: multilingual
  data_files:
  - split: test
    path: multilingual/inputs.json
---

# Tokenizer conformance fixtures

Reference inputs and Python fast-tokenizer outputs for tokenizer implementations.
The initial corpus contains **83 inputs in 30 categories, with 498 reference encodings across six tokenizers**.
This is a regression dataset, not a model-quality benchmark.

## Provenance and attribution

The input corpus and reference entries come from [apocryphx's swift-transformers PR #360](https://github.com/huggingface/swift-transformers/pull/360),
at commit [`ce847085784bacd8c3c15180c976b17c8ce73e31`](https://github.com/apocryphx/swift-transformers/tree/ce847085784bacd8c3c15180c976b17c8ce73e31/Tests/TokenizersTests/Resources/MultilingualConformance).
The corpus originated in [ObjCTokenizer](https://github.com/apocryphx/ObjCTokenizer/tree/main/Conformance)
and diagnosed the Unicode tokenization bugs documented in [swift-transformers #352](https://github.com/huggingface/swift-transformers/issues/352).
Daisuke Majima ([john-rocky](https://github.com/john-rocky)) contributed test-design ideas in PR #357, including stored decoded forms and the ungated TinyLlama reference model.
The original contributor documented AI assistance in the linked issue and PR.
The Apache 2.0 license from the source repository is included in `LICENSE`.

The 83 input records are unchanged. All six original baseline entry arrays were reproduced exactly
with the versions and model commits listed here before publication. Baseline metadata was expanded
to record those commits and the Rust tokenizers version.
`Tools/generate_tokenizer_baselines.py` is adapted from the contributor's generator.

## Layout

- `manifest.json`: corpus IDs, input paths, and baseline paths with model IDs and immutable model revisions.
- `multilingual/inputs.json`: records `{id, category, text}`; IDs are stable within the corpus. Preserve text exactly, including combining marks and whitespace.
- `multilingual/baselines/*.json`: `{metadata, entries}` for each tokenizer.
- `Tools/generate_tokenizer_baselines.py` and `Tools/requirements.txt`: regeneration and verification.

Each baseline entry has `id`, `input_ids`, `tokens`, `decoded_with_special`, and `decoded_skip_special`.
Metadata records `model_id`, `model_revision`, `transformers_version`, `tokenizers_version`,
`generated_at`, and `input_count`.
Generation uses `transformers.AutoTokenizer` with `use_fast=True` and `add_special_tokens=True`.
The Swift conformance test currently compares token IDs; tokens aid diagnostics and decoded forms
are retained for future decoder tests.

| Model | Tokenizer family |
| --- | --- |
| BAAI/bge-small-en-v1.5 | WordPiece |
| google-t5/t5-small | Unigram |
| openai-community/gpt2 | Byte-level BPE |
| FacebookAI/roberta-base | Byte-level BPE with RoBERTa postprocessing |
| Qwen/Qwen2.5-0.5B | Byte-level BPE |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | BPE with byte fallback |

## Reproduce or verify

From a checkout or downloaded snapshot of this dataset, using Python 3.12 and uv:

```sh
uv run --python 3.12 --with-requirements Tools/requirements.txt python Tools/generate_tokenizer_baselines.py --check
uv run --python 3.12 --with-requirements Tools/requirements.txt python Tools/generate_tokenizer_baselines.py
```

`--check` compares every generated entry (IDs, tokens and both decoded forms), returns nonzero on
a mismatch, and writes nothing. It ignores metadata such as the generation timestamp.
Omit `--check` to regenerate all baselines with fresh provenance metadata. Models are loaded at the immutable revisions in `manifest.json`, never implicitly
at `main`. The first run downloads tokenizer files; model weights are not needed.

## Swift consumption and expansion

Download a **pinned dataset commit** with `HubApi.snapshot(from: Hub.Repo(id: "pcuenq/tokenizer-conformance", type: .datasets), revision: ..., matching: "*.json")`.
The Swift suite reads the manifest and validates unique IDs, exact corpus coverage, model revisions,
and reference lengths before comparing output. Hub caching avoids downloading unchanged files on each run.
Network or malformed-data errors fail tests rather than silently skipping coverage.

To add cases, append stable IDs to a corpus and regenerate all its baselines. To add a model or a
separate corpus, extend the manifest and run the generator. Review the reference diff, publish a new
dataset commit, and explicitly update the consumer's pinned revision.

Do not replace Python expectations with Swift output. Implementation-specific known divergences
belong in the consuming test suite. At initial verification, Swift matched 490/498 encodings; the eight
already tracked in PR #360 remain (three Qwen Thai cases and five TinyLlama whitespace cases).
All entries remain in this dataset, including those eight, with the original Python expectations.