# bert-small-pii-detection by Gravitee.io: Open-Weight Model Source: https://savrn.com/models/bert-small-pii-detection Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve bert-small-pii-detection (29M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [bert-small-pii-detection on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/bert-small-pii-detection/gpus) ## SAVRN's Notes on bert-small-pii-detection Gravitee built this to find personal data in English text so it can be redacted before it leaves your systems. It is tiny, 29M parameters on a BERT small base, which is exactly right for its job: it can sit inline in an API gateway or a logging pipeline without adding meaningful latency. Apache 2.0 allows commercial use. PII detection is a recall problem, and a model this small will miss things, so pair it with pattern rules for structured identifiers such as card numbers and national IDs. Use it to catch the free-text cases rules cannot, then audit a sample of what it lets through. SAVRN Research, 2026-09-18 ## Model Card By Gravitee.io, published under apache-2.0, revision f8c27a85c51c. Token-classification model for PII detection, fine-tuned from prajjwal1/bert-small on [gravitee-io/pii-detection-dataset](https://huggingface.co/datasets/gravitee-io/pii-detection-dataset). ### Label Set ``` AGE, COORDINATE, CREDIT_CARD, DATE_TIME, EMAIL_ADDRESS, FINANCIAL, HONORIFIC, IBAN_CODE, IMEI, IP_ADDRESS, LOCATION, MAC_ADDRESS, NRP, ORGANIZATION, PASSWORD, PERSON, PHONE_NUMBER, TITLE, URL, US_BANK_NUMBER, US_DRIVER_LICENSE, US_ITIN, US_LICENSE_PLATE, US_PASSPORT, US_SSN ``` ### How to Use #### Quick start (pipeline) ``` from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline repo = "gravitee-io/bert-small-pii-detection" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForTokenClassification.from_pretrained(repo) pipe = pipeline("token-classification", model=model, tokenizer=tok, aggregation_strategy="simple") text = "Contact John Smith at john@example.com" pipe(text) ``` #### ONNX ``` pip install transformers onnxruntime huggingface_hub ``` ``` from huggingface_hub import hf_hub_download from transformers import AutoTokenizer, AutoConfig import onnxruntime as ort model_id = "gravitee-io/bert-small-pii-detection" tokenizer = AutoTokenizer.from_pretrained(model_id) id2label = AutoConfig.from_pretrained(model_id).id2label session = ort.InferenceSession(hf_hub_download(model_id, "model.quant.onnx")) text = "Contact John Smith at john@example.com" enc = tokenizer(text, return_tensors="np") inputs = {"input_ids": enc["input_ids"], "attention_mask": enc["attention_mask"]} logits = session.run(None, inputs)[0][0] tokens = tokenizer.convert_ids_to_tokens(enc["input_ids"][0]) labels = [id2label[i] for i in logits.argmax(-1)] for tok, label in zip(tokens, labels): print(f"{tok:<20} {label}") ``` ### Intended use [Read the full model card (2,369 words)](https://savrn.com/models/bert-small-pii-detection/card) ## Configuration Architecture BertForTokenClassification Context length (tokens) 512 Layers 4 Hidden size 512 Feed-forward size 2,048 Attention heads 8 Vocabulary size 30,522 Model type bert ## Identity and Version Repository gravitee-io/bert-small-pii-detection Publisher Gravitee.io Task Token classification Modality Text Library Not stated by the source Parameters 29M parameters Languages en Revision f8c27a85c51c0168f07b9dcf00265bf0a4097939 First published 2025-09-17 Last updated 2026-05-21 ## Files and Weights 10 files, 258.0 MB in total. The weights are 3 files totalling 257.0 MB in onnx, safetensors. Weights3 files · 257.0 MB Configuration2 files · 3.7 KB Tokenizer3 files · 944.3 KB Documentation1 file · 13.5 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.onnx | Weights | 114.2 MB | 5fc1642b7400 | | model.quant.onnx | Weights | 28.7 MB | b227845ff498 | | model.safetensors | Weights | 114.1 MB | a6319abf8718 | | config.json | Configuration | 3.0 KB | — | | special_tokens_map.json | Configuration | 695 B | — | | README.md | Documentation | 13.5 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 711.4 KB | — | | tokenizer_config.json | Tokenizer | 1.4 KB | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 257.0 MB [Download from Gravitee.io](https://huggingface.co/gravitee-io/bert-small-pii-detection) Released by Gravitee.io through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from prajjwal1/bert-small - Described by [arXiv:1908.08962](https://savrn.com/papers/well-read-students-learn-better-on-the-importance-of-pre-training-compact-models) - Described by [arXiv:2110.01518](https://savrn.com/papers/generalization-in-nli-ways-not-to-go-beyond-simple-heuristics) - Quantized from prajjwal1/bert-small - Trained on (disclosed) gravitee-io/pii-detection-dataset ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 257.0 MB | | 16-bit | 0.1 GB | | 8-bit | 0.0 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About bert-small-pii-detection ### How much GPU memory does bert-small-pii-detection need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (29M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run bert-small-pii-detection on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use bert-small-pii-detection commercially? Yes. bert-small-pii-detection is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is bert-small-pii-detection's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Token classification ### [bge-small-ner-early-stopping](https://savrn.com/models/bge-small-ner-early-stopping) [Arslan Yamaletdinov](https://savrn.com/model-publishers/arslanyam) This model is a fine-tuned version of BAAI/bge-small-en-v1.5 on an unknown dataset. 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This work was supported in part by the Medical Imaging and Data Resource Center (MIDRC), which is funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under contract 75N92020D00021 and through The Advanced Research Projects Agency for Health (ARPA-H) Open weights mit 512 tokens transformers [View model](https://savrn.com/models/stanford-deidentifier-base) ## Gravitee.io [All models and datasets](https://savrn.com/model-publishers/gravitee-io) ## Versions - [f8c27a85c51c](https://savrn.com/models/bert-small-pii-detection/versions/f8c27a85c51c) · current 2026-09-18 ## Explore More - [All token classification models](https://savrn.com/models/tasks/token-classification) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-20. - [Hugging Face record](https://huggingface.co/gravitee-io/bert-small-pii-detection) - [How the hub is built](https://savrn.com/model-hub/methodology)