| --- |
| 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. |
|
|
