knowledgator / gliner-stream-pii-v1.0

huggingface.co
Total runs: 126
24-hour runs: 0
7-day runs: -86
30-day runs: 30
Model's Last Updated: August 05 2026
token-classification

Introduction of gliner-stream-pii-v1.0

Model Details of gliner-stream-pii-v1.0

GLiNER Streaming PII — Qwen3 0.6B

An open-label PII detector built with the GLiNER streaming-span architecture and a Qwen3-0.6B causal backbone. It supports regular full-text inference, cached incremental streams, and full-session recomputation. This model was developed in collaboration between Wordcab and Knowledgator . For enterprise-ready, specialized PII/PHI/PCI models, contact us at [email protected] .

Architecture

Following the component schema in the GLiNER architecture docs , this checkpoint is composed of:

GLiNER Streaming Span architecture

Install and load
pip install "gliner>=0.2.28"
import torch
from gliner import GLiNER

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = "bf16" if device == "cuda" else "fp32"

model = GLiNER.from_pretrained(
    "knowledgator/gliner-stream-pii-v1.0",
    load_tokenizer=True,
    map_location=device,
    dtype=dtype,
).eval()

labels = [
    "person",
    "email address",
    "phone number",
    "street address",
    "credit card number",
    "passport number",
]
Three inference strategies
Strategy API What is computed Best for
1. Stateless full text No session_id Complete input and label prompt Documents, batches, independent requests
2. Cached incremental session_id=[id] New chunk only; decoder KV, labels, words, and span history are reused Live chat, ASR, logs, token streams
3. Full recompute session_id=[id], recompute=True Accumulated session plus new chunk; cache and all spans are rebuilt Final pass, changed labels, correction after drift
1. Stateless full text
entities = model.predict_entities(
    "Jane Doe can be reached at [email protected].",
    labels,
    threshold=0.5,
)
2. Cached incremental session
session_id = "call-42"

for chunk in [
    "Customer Jane",
    " Doe asked us to call",
    " +1 (415) 555-0132.",
]:
    snapshot = model.inference(
        [chunk],
        labels,
        session_id=[session_id],
        threshold=0.5,
    )[0]
    print(snapshot)
3. Full-session recompute
# The next chunk must be non-empty. This reruns all accumulated text
# and also permits a changed label set.
final_labels = labels + ["account number"]
final_snapshot = model.inference(
    [" Account 12345678 was also mentioned."],
    final_labels,
    session_id=[session_id],
    recompute=True,
    threshold=0.5,
)[0]

model.clear_session(session_id)

Streaming details:

  • Each call returns the complete current session snapshot , not only new entities.
  • Chunks are concatenated verbatim; preserve boundary spaces and punctuation.
  • Offsets refer to the complete accumulated text.
  • Keep labels fixed unless using recompute=True ; always clear finished sessions.
Evaluation

PIIMB ranking scores are label-agnostic, character-level masking metrics. They are micro-averaged within each task; group rows are unweighted averages across tasks. F2 is primary because it weights recall more heavily. Strict NER F1 also requires matching entity boundaries and type.

Scope / task Precision Recall Masking F1 Masking F2 FPR Strict NER F1
English average 87.36% 91.55% 89.18% 90.53% 3.01%
Multilingual average 53.84% 78.32% 60.45% 68.21% 5.85%
ai4privacy-en 94.69% 95.16% 94.92% 95.06% 1.52% 67.99%
ai4privacy-multi 87.34% 92.96% 90.07% 91.78% 3.82% 55.39%
gretel 86.95% 95.58% 91.06% 93.72% 5.20% 67.38%
mapa-eur-lex 20.34% 63.67% 30.83% 44.65% 7.88% 10.91%
nemotron-pii 72.75% 88.07% 79.68% 84.51% 4.98% 68.93%
privy 95.07% 87.39% 91.07% 88.83% 0.33% 81.68%

Configuration: PIIMB v0.3.0, dataset revision 4a13e9ffe6fd0d275efbde8afd4d8d8f1ffc2133 , sentences subset, threshold 0.5, bfloat16, evaluated 2026-07-24.

The main weakness is multilingual legal and administrative text: mapa-eur-lex reaches only 44.65% masking F2. Validate on the target languages, domains, labels, and threshold before deployment.

References

Runs of knowledgator gliner-stream-pii-v1.0 on huggingface.co

126
Total runs
0
24-hour runs
-2
3-day runs
-86
7-day runs
30
30-day runs

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