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]
.
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.
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.
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.
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