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 info@knowledgator.com.
Architecture
Following the component schema in the GLiNER architecture docs, this checkpoint is composed of:
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 jane.doe@example.com.",
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.