BCCard/MoAI-Privacy-Filter-INT8

MoAI-Privacy-Filter-INT8

MoAI-Privacy-Filter-INT8 is an INT8 weight-only ONNX Runtime artifact for Korean and English entity detection. It identifies 29 entity types with 117 BIOES token classes.

This repository contains the quantized ONNX graph, external tensor data, tokenizer, label configuration, taxonomy, and Viterbi calibration. It does not contain PyTorch weights and cannot be loaded with AutoModelForTokenClassification.from_pretrained().

1. Model summary

• —Base model: `openai/privacy-filter`.
• —Parent checkpoint: BCCard/MoAI-Privacy-Filter v3 final-bf16.
• —Task: Korean and English token classification with character-offset entity reconstruction.
• —Taxonomy: 29 entity labels and 117 BIOES classes under 4N+1.
• —Input policy: Up to 1024 tokens per sequence.
• —Quantization: INT8 weight-only with FP32 activations and logits.
• —Runtime: ONNX Runtime CPU execution.

The validated serving chain is:

text
-> tokenizer
-> INT8 weight-only ONNX graph
-> FP32 logits
-> constrained BIOES Viterbi decoding
-> character-offset spans
-> whitespace boundary refinement

2. Labels

The model detects the following entity types:

Each entity has B-, I-, E-, and S- boundary classes. The remaining class is O.

3. Usage

Install the required packages:

pip install "onnxruntime>=1.28,<1.29" "huggingface-hub>=1.5" "transformers>=5.6" numpy

The following example runs the graph and returns FP32 logits:

import json
from pathlib import Path

import numpy as np
import onnxruntime as ort
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer

model_id = "BCCard/MoAI-Privacy-Filter-INT8"
model_dir = Path(snapshot_download(repo_id=model_id))

tokenizer = AutoTokenizer.from_pretrained(model_dir)
session = ort.InferenceSession(
    str(model_dir / "model_quantized.onnx"),
    providers=["CPUExecutionProvider"],
)

text = "연락처는 010-1234-5678이고 접속 주소는 192.0.2.15입니다."
encoded = tokenizer(
    text,
    add_special_tokens=False,
    truncation=True,
    max_length=1024,
    return_tensors="np",
)
feeds = {
    model_input.name: np.asarray(encoded[model_input.name], dtype=np.int64)
    for model_input in session.get_inputs()
}
logits = session.run(["logits"], feeds)[0]

config = json.loads((model_dir / "config.json").read_text(encoding="utf-8"))
id2label = {
    int(class_id): label
    for class_id, label in config["id2label"].items()
}

print(logits.shape)
print(id2label[int(logits[0, 0].argmax())])

The graph emits logits rather than final entities. Apply the constrained BIOES Viterbi decoder using config.json and viterbi_calibration.json, then map token predictions to character offsets. Independent token argmax is not equivalent to the decoding chain used for validation.

4. Evaluation

The INT8 artifact and its FP32 ONNX reference were evaluated on the same 14,524-row Korean and English validation split. Metrics use strict character-span matching after constrained BIOES Viterbi decoding and whitespace boundary refinement.

The INT8 predictions achieved 0.9984 strict micro F1 against the FP32 ONNX predictions, with 14,402 of 14,524 rows matching exactly. All configured quantization quality gates passed.

These end-to-end character-span scores are not directly comparable with training-time token metrics because they include character reconstruction, constrained decoding, and boundary refinement.

5. Artifact size

The comparison uses the publicly released BF16 parent checkpoint as its reference. Actual memory use and latency depend on hardware, ONNX Runtime version, sequence length, and batch size.

6. Intended use and limitations

This model is intended for entity detection in privacy filtering, data review, and preprocessing workflows. A consuming application must define its own downstream redaction or retention policy for every detected label.

• —Evaluate the model on representative in-domain data before deployment.
• —Inputs longer than 1024 tokens require chunking and span reconciliation.
• —Context-dependent and ambiguous identifiers can still produce false positives or false negatives.
• —PORT and ZIPCODE, ORGANIZATION and PERSON, and structurally similar numeric identifiers require particular monitoring.
• —Do not treat model output as a substitute for organizational privacy controls or human review in high-risk workflows.

7. Training data and attribution

The parent model was trained with `BCCard/privacy-filter-openpii-masking`, which is derived in part from `ai4privacy/pii-masking-openpii-1.5m` and supplemented with Korean and English synthetic scenarios.

The base model is `openai/privacy-filter`. Review the licenses and usage terms of the model, dataset, and dependencies before redistribution or deployment.

8. License, Attribution, and Citation

@misc{bccard2026moaiprivacyfilter,
  title        = {MoAI Privacy Filter INT8: A Korean Finance-Domain PII Detection Model},
  author       = {BC Card AX Team},
  year         = {2026},
  howpublished = {https://huggingface.co/BCCard/MoAI-Privacy-Filter-INT8},
  note         = {INT8 weight-only ONNX artifact of a full fine-tune of openai/privacy-filter}
}

Related resources:

• —Base model - `openai/privacy-filter`.
• —Parent BF16 model - `BCCard/MoAI-Privacy-Filter`.
• —Training dataset - `BCCard/privacy-filter-openpii-masking`.
• —Source data attribution - `ai4privacy/pii-masking-openpii-1.5m` under CC-BY-4.0.