| --- |
| license: mit |
| language: |
| - en |
| - de |
| - fr |
| - it |
| - nl |
| - multilingual |
| base_model: oliverguhr/fullstop-punctuation-multilingual-base |
| pipeline_tag: token-classification |
| library_name: coreml |
| tags: |
| - coreml |
| - punctuation-restoration |
| - token-classification |
| - xlm-roberta |
| - on-device |
| --- |
| |
| # fullstop-coreml |
|
|
| `oliverguhr/fullstop-punctuation-multilingual-base` (XLM-RoBERTa token-classification, |
| ~0.3 B) converted to **CoreML** (fp16) for on-device punctuation restoration — |
| downloaded on first use and run locally through CoreML + `swift-transformers`. |
|
|
| This is a **format conversion only** — the weights are unchanged from the upstream |
| PyTorch model. The model is a verbatim **token classifier**: it emits one punctuation |
| label per token, so it can add punctuation (`. , ? - :`) but can never reword the |
| input. Capitalization is handled separately (a heuristic restorer is typically chained |
| after it). |
|
|
| ## Label map (`id2label`) |
|
|
| | id | label | meaning | |
| |----|-------|---------| |
| | 0 | `""` | no punctuation | |
| | 1 | `.` | period | |
| | 2 | `,` | comma | |
| | 3 | `?` | question mark | |
| | 4 | `-` | dash | |
| | 5 | `:` | colon | |
|
|
| ## Files (repo layout) |
|
|
| The repo root is the folder a loader consumes directly: |
|
|
| | File | Purpose | |
| |------|---------| |
| | `Fullstop.mlpackage/` | CoreML model bundle (compile before load) | |
| | `tokenizer.json` | XLM-R Unigram tokenizer (HuggingFace `tokenizers`) | |
| | `tokenizer_config.json` | tokenizer config | |
| | `special_tokens_map.json` | XLM-R special tokens (`<s>`, `</s>`, `<pad>`, `<unk>`, `<mask>`) | |
| | `config.json` | model config incl. `id2label` / `label2id` | |
|
|
| ## Loading (Swift) |
|
|
| ```swift |
| // 1. Snapshot the repo (swift-transformers Hub): |
| // Hub.snapshot(from: Repo(id: "gregbarbosa/fullstop-coreml")) -> localDir |
| // 2. Compile + load the CoreML model, build the tokenizer from the same folder: |
| let compiled = try await MLModel.compileModel(at: localDir.appendingPathComponent("Fullstop.mlpackage")) |
| let model = try MLModel(contentsOf: compiled) |
| let tokenizer = try await AutoTokenizer.from(modelFolder: localDir) |
| ``` |
|
|
| Inference contract: feed `input_ids` (shape `[1, seq]`, natural length — no padding) |
| and `attention_mask`; the output logits are `[1, seq, 6]` → argmax per token yields the |
| label id above. Attach each **word's** punctuation from the label on its final subword |
| (token boundaries marked by the XLM-R `▁` U+2581 prefix); skip special tokens. |
|
|
| ## Provenance |
|
|
| - **Source model:** [`oliverguhr/fullstop-punctuation-multilingual-base`](https://huggingface.co/oliverguhr/fullstop-punctuation-multilingual-base) (MIT) |
| - **Conversion:** PyTorch → ONNX → CoreML (fp16). Parity verified 15/15 labels identical |
| to the PyTorch reference across the conversion fixtures; tokenizer parity re-verified |
| on-device 2026-06-26. |
| - **Conversion harness:** `restore-bench` (standalone repo). |
|
|
| ## License |
|
|
| MIT — same as the upstream model. Conversion produced by Greg Barbosa. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{guhr-EtAl:2021:fullstop, |
| title={FullStop: Multilingual Deep Models for Punctuation Prediction}, |
| author={Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and B{\"o}hme, Hans Joachim}, |
| booktitle={Proceedings of the Swiss Text Analytics Conference 2021}, |
| month={June}, |
| year={2021}, |
| address={Winterthur, Switzerland}, |
| publisher={CEUR Workshop Proceedings}, |
| url={http://ceur-ws.org/Vol-2957/sepp_paper4.pdf} |
| } |
| ``` |
|
|
