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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,148 @@
+---
+library_name: gliner
+pipeline_tag: token-classification
+base_model: Qwen/Qwen3-0.6B
+tags:
+  - gliner
+  - gliner-streaming
+  - named-entity-recognition
+  - zero-shot-ner
+  - pii
+  - privacy
+  - multilingual
+language:
+  - en
+metrics:
+  - precision
+  - recall
+  - f1
+---
+
+# 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.
+
+## Architecture
+
+Following the component schema in the [GLiNER architecture docs](https://github.com/urchade/GLiNER/blob/main/docs/add_custom_architecture.md), this checkpoint is composed of:
+
+[![GLiNER Streaming Span architecture](assets/gliner-streaming-architecture.svg)](assets/gliner-streaming-architecture.svg)
+
+[Open the full-size architecture diagram](assets/gliner-streaming-architecture.svg).
+
+## Install and load
+
+```bash
+pip install "gliner>=0.2.27"
+```
+
+```python
+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(
+    "/path/to/qwen-pii-final",  # or the Hugging Face model ID
+    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
+
+```python
+entities = model.predict_entities(
+    "Jane Doe can be reached at jane.doe@example.com.",
+    labels,
+    threshold=0.5,
+)
+```
+
+### 2. Cached incremental session
+
+```python
+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
+
+```python
+# 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
+- [Blog](https://medium.com/p/11aefa0e4ad8/)
+- [GLiNER paper](https://arxiv.org/abs/2311.08526)
+- [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)
+- [PIIMB benchmark](https://huggingface.co/datasets/piimb/pii-masking-benchmark)
+- [GLiNER repository](https://github.com/urchade/GLiNER)

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

gliner_config.json

@@ -0,0 +1,147 @@
+{
+  "class_token_index": 151669,
+  "decoder_config": {
+    "_name_or_path": "Qwen/Qwen3-0.6B",
+    "architectures": [
+      "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "chunk_size_feed_forward": 0,
+    "dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_types": [
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 40960,
+    "max_window_layers": 28,
+    "model_type": "qwen3",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "pad_token_id": null,
+    "problem_type": null,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "rope_theta": 1000000,
+      "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151671
+  },
+  "dropout": 0.3,
+  "embed_ent_token": true,
+  "encoder_config": null,
+  "ent_token": "<<ENT>>",
+  "eos_token_id": 151645,
+  "fine_tune": true,
+  "fuse_layers": false,
+  "hidden_size": 1024,
+  "id_to_classes": null,
+  "label_token": "<<LABEL>>",
+  "labels_encoder_config": {
+    "attention_probs_dropout_prob": 0.1,
+    "bos_token_id": null,
+    "eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-07,
+    "legacy": true,
+    "max_position_embeddings": 512,
+    "max_relative_positions": 512,
+    "model_type": "deberta-v2",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 2,
+    "pad_token_id": 0,
+    "pooler_dropout": 0.0,
+    "pooler_hidden_act": "gelu",
+    "pooler_hidden_size": 1024,
+    "pos_att_type": [
+      "p2c",
+      "c2p"
+    ],
+    "position_biased_input": true,
+    "relative_attention": true,
+    "tie_word_embeddings": true,
+    "type_vocab_size": 0,
+    "vocab_size": 128100
+  },
+  "max_cache_length": null,
+  "max_len": 8192,
+  "max_neg_type_ratio": 1,
+  "max_types": 100,
+  "max_width": 12,
+  "model_name": "Qwen/Qwen3-0.6B",
+  "model_type": "gliner_streaming_span",
+  "name": "streaming span gliner",
+  "neg_spans_ratio": 1.0,
+  "num_post_fusion_layers": 1,
+  "num_rnn_layers": 0,
+  "pad_token_id": 151643,
+  "post_fusion_schema": "",
+  "precomputed_prompts_mode": null,
+  "represent_spans": false,
+  "right_context_width": 12,
+  "sep_token": "<<SEP>>",
+  "sep_token_index": 151670,
+  "span_encoder_config": null,
+  "span_loss_coef": 1.0,
+  "span_mode": "markerV2",
+  "subtoken_pooling": "first",
+  "token_loss_coef": 1.0,
+  "transformers_version": "5.6.2",
+  "use_cache": false,
+  "vocab_size": 151671,
+  "words_splitter_type": "whitespace"
+}

pytorch_model.bin

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+oid sha256:a7f37c8aa4317954ceaf2c7e83bfa45dd6279d04b589d163f1ecf6f30c3df36f
+size 2706436847

tokenizer.json

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+oid sha256:293208942fa5cad773139afda2d0c2819f2442d5c8fca7f2ce9d22c326b0881b
+size 11423773

tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}