SporeLabs Scrub

.gitattributes

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LICENSE-CODE

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+MIT License
+
+Copyright (c) 2026 SporeLabs
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
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+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

NOTICE.md

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+# Notices
+
+The model weights are released under the Apache License 2.0 (`LICENSE`). The `sporelabs-scrub` code is MIT
+(`LICENSE-CODE`).
+
+## Base model
+
+Ettin encoder, `jhu-clsp/ettin-encoder-17m`, by Johns Hopkins University, Center for Language and Speech Processing,
+released under the MIT licence. The weights here are a fine-tuned derivative.
+
+```
+MIT License
+
+Copyright (c) 2025 Johns Hopkins University Center for Language and Speech Processing
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+## Training data credits
+
+- NVIDIA Nemotron-PII (`nvidia/Nemotron-PII`), CC BY 4.0.
+- Gretel PII masking EN v1 (`gretelai/gretel-pii-masking-en-v1`), Apache-2.0.
+- WildChat-1M (`allenai/WildChat-1M`), Allen Institute for AI, ODC-BY 1.0. Zhao et al., "WildChat: 1M ChatGPT
+  Interaction Logs in the Wild", ICLR 2024. User turns only.
+- SMS Spam Collection (Almeida and Hidalgo), UCI Machine Learning Repository, CC BY 4.0, https://doi.org/10.24432/C5CC84
+- MASSIVE 1.1 (FitzGerald et al., Amazon), CC BY 4.0.
+- OpenAssistant Conversations, oasst2 (`OpenAssistant/oasst2`), Apache-2.0.
+- Keycloak message templates (https://github.com/keycloak/keycloak), Apache-2.0.
+- GitHub source files under MIT, Apache-2.0, BSD, ISC, CC0 and Unlicense licences, via `codeparrot/github-code-clean`.
+
+Personal details in the public text were replaced with generated values before training. The weights hold no
+training text.

PARITY.md

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+# ONNX parity
+
+`onnx/model.onnx` and `onnx/model_int8.onnx` were exported from the PyTorch weights in this repository (opset 17,
+dynamic batch and length) and checked with onnxruntime 1.30.0 against PyTorch 2.8.0 on the 160 development documents
+of the SporeLabs Scrub benchmark (716 marked items), running the full scrubber (rules + model).
+
+| graph | size | largest logit difference | findings identical to PyTorch | secrets recall | direct-identifier recall | precision |
+|---|---|---|---|---|---|---|
+| PyTorch | | | | 0.986 | 0.954 | 0.878 |
+| fp32 ONNX | 67 MB | 0.00004 | 796 of 796 | 0.986 | 0.954 | 0.878 |
+| int8 ONNX | 19 MB | 3.1 | 782 of 796 | 0.986 | 0.948 | 0.883 |
+
+fp32 gives the same findings as PyTorch and is the default. int8 is a quarter of the size and catches slightly fewer
+direct identifiers; use it where size matters more (`Scrub(precision="int8")`).

README.md

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+---
+license: apache-2.0
+base_model: jhu-clsp/ettin-encoder-17m
+language:
+  - en
+library_name: onnxruntime
+pipeline_tag: token-classification
+tags:
+  - pii
+  - redaction
+  - anonymization
+  - secrets
+  - privacy
+  - onnx
+---
+
+# SporeLabs Scrub
+
+Scrub finds personal details and secrets in text and tells you what each one is: a name, an address, a phone number,
+an email, a password, a one-time code, an API key. You can replace them with tags, with consistent stand-ins of the
+same shape, or with asterisks, or just read the list.
+
+It is a 17M-parameter model plus rules, small enough to run on one laptop CPU thread: about 2 ms for a chat
+message.
+
+## Use it
+
+**Python package** (onnxruntime, no PyTorch):
+
+```
+pip install "sporelabs-scrub @ https://huggingface.co/SporeLabs/scrub/resolve/main/package/sporelabs_scrub-0.1.0-py3-none-any.whl"
+```
+
+```python
+from sporelabs_scrub import Scrub
+
+s = Scrub()  # downloads this repository's ONNX model once, then runs offline
+s.find("Call Dana Whitlock on +1 415 867 5309")
+# [{'start': 5, 'end': 18, 'type': 'PERSON', 'text': 'Dana Whitlock'},
+#  {'start': 22, 'end': 37, 'type': 'PHONE', 'text': '+1 415 867 5309'}]
+
+s.scrub(text, mode="tag")        # Call [PERSON] on [PHONE]
+s.scrub(text, mode="pseudonym")  # Call Novak Rosa on +1 128 857 1946  (same input, same stand-in)
+s.scrub(text, mode="mask")       # Call **** ******** on ** *** *** ****
+```
+
+From the command line: `echo "my password is Tq9vLm2x8RwZ" | sporelabs-scrub` prints `my password is [PASSWORD]`;
+`sporelabs-scrub --find` prints the findings as JSON.
+
+**Download the files** without the package:
+
+```
+hf download SporeLabs/scrub --local-dir scrub
+```
+
+**SporeLabs API**: `POST https://sporelabs.dev/v1/scrub`, $0.50 per million characters.
+
+```bash
+curl https://sporelabs.dev/v1/scrub \
+  -H "Authorization: Bearer $SPORELABS_API_KEY" -H "Content-Type: application/json" \
+  -d '{"text": "Call Dana Whitlock on +1 415 867 5309", "mode": "tag"}'
+```
+
+## What it identifies
+
+| type | what |
+|---|---|
+| `PERSON` | people's names, including names that are also words ("Will", "Grace") |
+| `ADDRESS` | street addresses, with unit, postcode and town |
+| `PHONE` | phone numbers in international and national formats, also spoken ("four one five...") |
+| `EMAIL` | email addresses, also encoded or spoken ("dana at fernhollow dot io") |
+| `USERNAME` | personal handles and logins (`@dana`, `ssh dana@host`, `-u dana`) |
+| `PATH_USER` | the user name in a home path (`/Users/dana/...`) |
+| `CREDIT_CARD`, `BANK`, `GOV_ID` | card numbers (Luhn-checked), IBANs, US social security numbers |
+| `PASSWORD` | passwords in prose, commands, config files and connection strings, also dictated |
+| `OTP` | one-time and verification codes, PINs, backup and recovery codes |
+| `URL_CREDENTIAL` | a password or token inside a URL |
+| `AWS_KEY`, `STRIPE_KEY`, `OPENAI_KEY`, `ANTHROPIC_KEY`, `HF_TOKEN`, `GITHUB_TOKEN`, `JWT` | provider keys and tokens, by format |
+| `OTHER_SECRET` | any other credential: private keys, bearer tokens, session cookies, webhook secrets, database passwords |
+
+Because each detail comes back with its type, Scrub does more than redact. You can route a support ticket by what it
+contains, fill CRM or intake fields from free text, list which kinds of personal data a system holds (for a data
+map or an access request), pseudonymise a dataset so the same person gets the same stand-in everywhere, and keep the
+rest of the text usable for analytics and AI.
+
+## How SporeLabs built it
+
+- **Public text.** SporeLabs took text written by people from public datasets: chats (WildChat), SMS messages,
+  spoken assistant requests in nine languages (MASSIVE), forum answers (OpenAssistant), open-source code and its
+  message templates. To these it added two public synthetic PII sets, NVIDIA Nemotron-PII and Gretel PII masking.
+- **Real details out, labelled stand-ins in.** SporeLabs found the personal details already in that text with its
+  rules and GLiNER2, replaced each with a generated value of the same kind (a name for a name, a valid-looking phone
+  number for a phone number), and labelled it. Text where the detectors disagreed was left out.
+- **The places details really turn up.** SporeLabs placed generated details into the formats they leak from: command
+  lines, HTTP headers, logs, config and `.env` files, CSV exports, chat exports, SMS inboxes with sign-in codes, git
+  history, email signatures, and spoken copies of messages as a speech recogniser writes them. It also added
+  look-alikes that are not sensitive (example keys, test card numbers, hashes, version strings) so the model learns
+  to leave them alone.
+- **Model.** SporeLabs fine-tuned the Ettin 17M encoder (Johns Hopkins University, MIT licence) with one output per
+  type per token, on about 305,000 documents in one pass, and picked each type's threshold on held-out data.
+- **Rules.** Anything with a format (provider keys, tokens, card numbers, codes after a code word, emails) is found
+  by rules, which take precedence; the model finds what has no format, such as names and addresses. A value found
+  once is found everywhere it repeats in the text.
+
+## Results
+
+SporeLabs Scrub benchmark, held-out set `test` (100 documents of chats, emails, tickets, logs, agent transcripts
+and config, 135 secrets, 504 direct identifiers, 798 look-alikes). "Caught" means every letter and
+digit of the item was covered. Each system ran with its own default settings.
+
+| system | size | secrets caught | direct identifiers caught | precision | look-alikes scrubbed (lower is better) |
+|---|---|---|---|---|---|
+| SporeLabs Scrub | 17M + rules | 0.918 | 0.921 | 0.810 | 0.094 |
+| OpenAI Privacy Filter | 1.4B | 0.852 | 0.770 | 0.773 | 0.175 |
+| GLiNER2-PII | 307M | 0.770 | 0.873 | 0.741 | 0.283 |
+| AWS Comprehend | cloud API | 0.385 | 0.855 | 0.781 | 0.148 |
+| Presidio | spaCy lg + rules | 0.074 | 0.544 | 0.578 | 0.167 |
+
+The development sets, used to tune the rules (SporeLabs Scrub):
+
+| set | documents | secrets caught (n) | direct identifiers caught (n) | precision | look-alikes scrubbed |
+|---|---|---|---|---|---|
+| `extra1` | 200 | 0.989 (265) | 0.932 (723) | 0.844 | 0.081 |
+| `extra2` | 200 | 0.987 (477) | 0.951 (740) | 0.840 | 0.079 |
+| `extra3` | 150 | 0.919 (308) | 0.922 (561) | 0.772 | 0.098 |
+| `dev` | 160 | 0.986 (216) | 0.954 (500) | 0.878 | 0.046 |
+
+Scrub is trained for the text that leaks from apps and developer tools: chats, messages, tickets, logs, code and
+config. General-purpose public PII sets are mostly forms, records and documents, and many also label dates, places and
+organisations, which Scrub leaves alone by design. On the personal details Scrub covers, it catches more than Presidio
+on most of them; GLiNER2-PII catches more than Scrub on each of the four SporeLabs ran it on. Every number is in the
+benchmark's `results/results.json`.
+
+Speed of the `sporelabs-scrub` package (ONNX, rules included), one CPU thread on an Apple M3:
+
+| input | fp32 | int8 |
+|---|---|---|
+| chat message (median 42 characters) | 2.1 ms | 1.4 ms |
+| document (median 665 characters) | 34.8 ms | 23.7 ms |
+
+Full results: [SporeLabs/scrub-benchmark](https://huggingface.co/datasets/SporeLabs/scrub-benchmark).
+
+## Intended use and limits
+
+Scrub is a first pass that removes most personal details and secrets before text is stored, shared, logged or used
+for training, and it labels what it found. It is not a guarantee: it misses some details, so text with legal or
+safety stakes needs a person or a second system to review it. It was built and tested on English text; it handles
+names, addresses and phone numbers from many countries, but other languages are less tested. It does not look for
+dates, organisations or places on their own, since in most text those are not personal.
+
+## Files
+
+| file | what |
+|---|---|
+| `onnx/model.onnx` | the model, fp32, 67 MB (the package default) |
+| `onnx/model_int8.onnx` | int8, 19 MB; slightly lower recall on names (see `PARITY.md`) |
+| `scrub_config.json`, `tokenizer.json` | types, thresholds, windowing; the tokenizer |
+| `encoder/`, `heads.safetensors`, `thresholds.json`, `kit_model.json` | the same model as PyTorch weights |
+| `package/` | the `sporelabs-scrub` wheel and source |
+
+Licences: weights Apache-2.0 (`LICENSE`), code MIT (`LICENSE-CODE`); credits in `NOTICE.md`.
+Training data: [SporeLabs/scrub-data](https://huggingface.co/datasets/SporeLabs/scrub-data). Benchmark:
+[SporeLabs/scrub-benchmark](https://huggingface.co/datasets/SporeLabs/scrub-benchmark).

SHA256SUMS

@@ -0,0 +1,21 @@
+c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4  LICENSE
+52f953e46ff901014a79db5f5b8d68f80004b0730163c1c5cc69416a0ca86291  LICENSE-CODE
+f2fbb955ccc1c194759ceac2b1fa3352c87259453b5cce0c30d74137724da266  NOTICE.md
+43a937a857874da90bf880356d8c8197f3f20916f9b3bd22a2ed9e5fdd1be38a  PARITY.md
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