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GLiNER2-PII-CoreAI

Token Classification
GLiNER
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core-ai
apple
on-device
ner
pii
deberta
Model card Files
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Mirror of mlboydaisuke/GLiNER2-PII-CoreAI

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Files changed (19) hide show
  1. .gitattributes +5 -0
  2. README.md +103 -0
  3. ios/extractor.json +21 -0
  4. ios/gliner2-pii_float16_s256_t96_m16.aimodel/main-h18p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/manifest.plist +1198 -0
  5. ios/gliner2-pii_float16_s256_t96_m16.aimodel/main-h18p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/original_model_0.mpsgraph +3 -0
  6. ios/gliner2-pii_float16_s256_t96_m16.aimodel/main-h18p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/resources.bin +3 -0
  7. ios/gliner2-pii_float16_s256_t96_m16.aimodel/main-h18p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/specialized_model_1.mpsgraph +3 -0
  8. ios/gliner2-pii_float16_s256_t96_m16.aimodel/main-h18p.mlirb +0 -0
  9. ios/gliner2-pii_float16_s256_t96_m16.aimodel/main.hash +1 -0
  10. ios/gliner2-pii_float16_s256_t96_m16.aimodel/metadata.json +9 -0
  11. ios/gliner2-pii_float16_s256_t96_m16.aimodel/stats.json +1 -0
  12. ios/tokenizer/tokenizer.json +3 -0
  13. ios/tokenizer/tokenizer_config.json +33 -0
  14. macos/extractor.json +21 -0
  15. macos/gliner2-pii_float16_s256_t96_m16.aimodel/main.hash +1 -0
  16. macos/gliner2-pii_float16_s256_t96_m16.aimodel/main.mlirb +3 -0
  17. macos/gliner2-pii_float16_s256_t96_m16.aimodel/metadata.json +7 -0
  18. macos/tokenizer/tokenizer.json +3 -0
  19. macos/tokenizer/tokenizer_config.json +33 -0
.gitattributes CHANGED
@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ ios/gliner2-pii_float16_s256_t96_m16.aimodel/main-h18p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/original_model_0.mpsgraph filter=lfs diff=lfs merge=lfs -text
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+ ios/gliner2-pii_float16_s256_t96_m16.aimodel/main-h18p-delegates/MPSGraph/mpsExecutable.mpsgraphpackage/specialized_model_1.mpsgraph filter=lfs diff=lfs merge=lfs -text
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+ ios/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ macos/gliner2-pii_float16_s256_t96_m16.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
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+ macos/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,103 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model:
4
+ - fastino/gliner2-privacy-filter-PII-multi
5
+ - microsoft/mdeberta-v3-base
6
+ pipeline_tag: token-classification
7
+ tags:
8
+ - core-ai
9
+ - coreai
10
+ - apple
11
+ - on-device
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+ - ner
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+ - pii
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+ - gliner
15
+ - deberta
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+ library_name: coreai
17
+ ---
18
+
19
+ > **Mirror** of [`mlboydaisuke/GLiNER2-PII-CoreAI`](https://huggingface.co/mlboydaisuke/GLiNER2-PII-CoreAI) — the canonical repo ([CoreAI Model Zoo](https://github.com/john-rocky/coreai-model-zoo)). Updates land there first.
20
+
21
+
22
+ # GLiNER2-PII — Core AI
23
+
24
+ The zoo's **first NER / schema-driven information-extraction** model, and its first **DeBERTa-v3**
25
+ (disentangled-attention) port. Zero-shot entity extraction: pass any label set at call time and the
26
+ model finds those entities in the text — the flagship use is **on-device PII redaction**.
27
+ [`fastino/gliner2-privacy-filter-PII-multi`](https://huggingface.co/fastino/gliner2-privacy-filter-PII-multi)
28
+ (Apache-2.0) on a multilingual [mDeBERTa-v3](https://huggingface.co/microsoft/mdeberta-v3-base) base
29
+ (278M), fused into **one static Core AI graph**; the tokenizer, schema linearization, and span
30
+ decode run in the Swift host.
31
+
32
+ > **Uncontested on iPhone.** An on-device GLiNER2 already exists (GLiNER2Swift) but it is
33
+ > macOS-CPU / MLX only. This runs the GPU on iPhone (and, AOT-compiled, the ANE) — the first GLiNER
34
+ > on Apple Silicon's accelerators.
35
+
36
+ ## Use it
37
+
38
+ Three lines with [**CoreAIKit**](https://github.com/john-rocky/coreai-kit) — `InformationExtractor`
39
+ downloads this bundle once, then runs fully offline:
40
+
41
+ ```swift
42
+ import CoreAIKitEmbeddings
43
+
44
+ let extractor = try await InformationExtractor(model: .gliner2PII)
45
+
46
+ // zero-shot: any labels you want, decided at call time
47
+ let entities = try await extractor.extract(
48
+ from: "Contact Dr. Sarah Johnson at sarah.j@acme.com or +1-415-555-0142.",
49
+ entities: ["person", "email", "phone number"])
50
+ // ["person": ["Sarah Johnson"], "email": ["sarah.j@acme.com"], "phone number": ["+1-415-555-0142"]]
51
+
52
+ // or redact in place
53
+ let clean = try await extractor.redact(
54
+ "SSN 123-45-6789, card 4111 1111 1111 1111.",
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+ entities: ["social security number", "credit card number"])
56
+ // "SSN [SOCIAL SECURITY NUMBER], card [CREDIT CARD NUMBER]."
57
+ ```
58
+
59
+ Runnable demo: **[Examples/InfoExtract ↗](https://github.com/john-rocky/coreai-kit/tree/main/Examples/InfoExtract)**
60
+ — a paste-text → detect-and-redact PII app (iOS + macOS CLI).
61
+
62
+ ## How it works
63
+
64
+ One fused static graph runs the whole model; the host handles the text↔schema plumbing that makes it
65
+ schema-agnostic.
66
+
67
+ - **Fused graph** — `forward(input_ids[1,256], attention_mask[1,256], text_word_idx[1,96],
68
+ schema_idx[1,17]) → span_scores[1,16,96,8]`. Inside: mDeBERTa-v3 (disentangled attention, exported
69
+ at a fixed shape so the relative-position buckets gather cleanly) → "first" sub-word pooling →
70
+ SpanMarker → CountLSTM → einsum → sigmoid. `MMAX=16` labels, `T=96` words, span width `K=8`.
71
+ - **Schema-agnostic** — the label set is **not** baked in. The host linearizes the user's labels
72
+ into the graph's `input_ids` (`( [P] entities ( [E] l0 [E] l1 … ) ) [SEP_TEXT] …`) and supplies the
73
+ gather indices, so a single converted bundle answers any schema up to `MMAX`.
74
+ - **Host collator** — mDeBERTa SentencePiece/Unigram tokenization + GLiNER word-split + schema
75
+ linearization + first-sub-word / schema-marker gather positions. Byte-identical to GLiNER2's Python
76
+ `collate_fn_inference`.
77
+ - **Host decode** — per-label threshold + confidence-descending greedy NMS over character spans,
78
+ byte-identical to GLiNER2 `_format_spans`.
79
+
80
+ ## Verification
81
+
82
+ Byte-gated against the reference GLiNER2 `ext.extract` at every tier — the Swift collator matches
83
+ Python `collate_fn_inference` (input ids + gather indices), the fp16 Core AI graph matches the fp32
84
+ reference (span-scores cos **0.999993**), and the decoded entities match exactly:
85
+
86
+ - **Python** — fp32 span-scores cos 1.0, decoded entities == `ext.extract`.
87
+ - **Swift on Mac GPU** — the demo PII suite decodes byte-identically; arbitrary runtime schemas
88
+ (credentials, org/money/date/location) also match `ext.extract` exactly.
89
+ - **iPhone 17 Pro** (A19 Pro, AOT h18p) — same suite, `GATE_RESULT: PASS`. Model load ~1.8 s;
90
+ extraction ~22–32 ms per text (warm).
91
+
92
+ ## Files
93
+
94
+ - `macos/` — JIT `.aimodel` (fp16, ~582 MB) + `tokenizer/` + `extractor.json`.
95
+ - `ios/` — AOT-compiled h18p bundle (~823 MB; the device JIT is skipped) + `tokenizer/` +
96
+ `extractor.json`.
97
+
98
+ `extractor.json` carries the graph shapes and the GLiNER special-marker token ids (they live above
99
+ the Unigram vocab, so the host emits them directly). The tokenizer is the mDeBERTa SentencePiece model
100
+ declared as `XLMRobertaTokenizer` so swift-transformers routes it through its Unigram implementation.
101
+
102
+ - Base model: [fastino/gliner2-privacy-filter-PII-multi](https://huggingface.co/fastino/gliner2-privacy-filter-PII-multi) (Apache-2.0), on [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) (MIT).
103
+ - Conversion + Swift-port reference: [CoreAI-Model-Zoo](https://github.com/john-rocky/CoreAI-Model-Zoo).
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