Update README.md

README.md

@@ -18,11 +18,17 @@ tags:
 - token-classification
 - guardrails
 ---
+# GLiNER Guard — Unified Multitask Guardrail
+One encoder model that replaces your entire guardrail stack: safety classification, PII detection, adversarial attack detection, intent and tone analysis — all in a single forward pass.
+![GLiNER Guard architecture](biencoder.png)
+
+**147M params · GLiNER2 · biencoder · modernbert multilingual · zero-shot classification, NER and more · no LLM required**
 
 ## Installation
-Install dependencies (now via our fork, wi'll update installation part after PR to GLiNER2 repo)
+Install dependencies\
+(now via our fork, wi'll update installation part after PR to GLiNER2 repo)
 ```bash
-pip install "gliner2 @ git+https://github.com/bogdanminko/GLiNER2.git@feature/bi-encoder" torch transformers
+pip install "gliner2 @ git+https://github.com/bogdanminko/GLiNER2.git@feature/bi-encoder"
 ```
 ## Usage
 Classify Harmful messages and Detect PII via single forward pass
@@ -52,4 +58,163 @@ output:
   'email': ['john.smith@gmail.com'],
   'phone': []},
  'safety': 'unsafe'}
-```
+```
+
+## Supported Tasks
+
+GLiNER Guard is purpose-built for 6 guardrail tasks via a shared encoder — no LLM required.\
+Thanks to zero-shot generalization, it can also handle custom labels outside the training taxonomy.
+
+| Task | Type | Labels | Key Labels |
+|------|------|--------|------------|
+| **Safety** | single-label | 2 | `safe` `unsafe` |
+| **PII / NER** | span extraction | 32 | `person` `email` `phone` `card_number` `address` |
+| **Adversarial Detection** | multi-label | 15 | `jailbreak_persona` `prompt_injection` `instruction_override` `data_exfiltration` |
+| **Harmful Content** | multi-label | 30 | `hate_speech` `violence` `child_exploitation` `fraud` `pii_exposure` |
+| **Intent** | single-label | 13 | `informational` `adversarial` `threatening` `solicitation` |
+| **Tone of Voice** | single-label | 10 | `neutral` `aggressive` `manipulative` `deceptive` |
+
+<details>
+<summary><b>Safety</b> — all 2 labels</summary>
+
+Classifies whether a message is safe or unsafe. Single-label.
+```python
+SAFETY_LABELS = ["safe", "unsafe"]
+```
+
+| Label | Description |
+|-------|-------------|
+| `safe` | Message does not contain harmful or policy-violating content |
+| `unsafe` | Message contains harmful, dangerous, or policy-violating content |
+
+</details>
+
+<details>
+<summary><b>NER / PII</b> — all 32 entity types</summary>
+
+Span extraction across 7 groups. Use labels from this list for best results — out-of-taxonomy labels may work via zero-shot generalization but are not benchmarked.
+
+| Group | Labels |
+|-------|--------|
+| **Person** | `person` `first_name` `last_name` `alias` `title` |
+| **Location** | `country` `region` `city` `district` `street` `building` `unit` `postal_code` `landmark` `address` |
+| **Organization** | `company` `government` `education` `media` `product` |
+| **Contact** | `email` `phone` `social_account` `messenger` |
+| **Identity** | `passport` `national_id` `document_id` |
+| **Temporal** | `date_of_birth` `event_date` |
+| **Financial** | `card_number` `bank_account` `crypto_wallet` |
+```python
+PII_LABELS = [
+    "person", "first_name", "last_name", "alias", "title",
+    "country", "region", "city", "district", "street",
+    "building", "unit", "postal_code", "landmark", "address",
+    "company", "government", "education", "media", "product",
+    "email", "phone", "social_account", "messenger",
+    "passport", "national_id", "document_id",
+    "date_of_birth", "event_date",
+    "card_number", "bank_account", "crypto_wallet",
+]
+```
+
+</details>
+
+<details>
+<summary><b>Adversarial Detection</b> — all 15 labels</summary>
+
+Detects attacks against LLM-based systems. Multi-label: a single message can combine multiple attack vectors.
+
+| Subgroup | Labels |
+|----------|--------|
+| **Jailbreak** | `jailbreak_persona` `jailbreak_hypothetical` `jailbreak_roleplay` |
+| **Injection** | `prompt_injection` `indirect_prompt_injection` `instruction_override` |
+| **Extraction** | `data_exfiltration` `system_prompt_extraction` `context_manipulation` `token_manipulation` |
+| **Advanced** | `tool_abuse` `social_engineering` `multi_turn_escalation` `schema_poisoning` |
+| **Clean** | `none` |
+```python
+ADVERSARIAL_LABELS = [
+    "jailbreak_persona", "jailbreak_hypothetical", "jailbreak_roleplay",
+    "prompt_injection", "indirect_prompt_injection", "instruction_override",
+    "data_exfiltration", "system_prompt_extraction", "context_manipulation", "token_manipulation",
+    "tool_abuse", "social_engineering", "multi_turn_escalation", "schema_poisoning",
+    "none",
+]
+```
+
+</details>
+
+<details>
+<summary><b>Harmful Content</b> — all 30 labels</summary>
+
+Detects harmful content categories. Multi-label: a message can belong to multiple categories simultaneously.
+
+| Subgroup | Labels |
+|----------|--------|
+| **Interpersonal** | `harassment` `hate_speech` `discrimination` `doxxing` `bullying` |
+| **Violence & Danger** | `violence` `dangerous_instructions` `weapons` `drugs` `self_harm` |
+| **Sexual & Exploitation** | `sexual_content` `child_exploitation` `grooming` `sextortion` |
+| **Deception** | `fraud` `scam` `social_engineering` `impersonation` |
+| **Sensitive Topics** | `profanity` `extremism` `political` `war` `espionage` `cybersecurity` `religious` `lgbt` |
+| **Information** | `misinformation` `copyright_violation` `pii_exposure` |
+| **Clean** | `none` |
+```python
+HARMFUL_LABELS = [
+    "harassment", "hate_speech", "discrimination", "doxxing", "bullying",
+    "violence", "dangerous_instructions", "weapons", "drugs", "self_harm",
+    "sexual_content", "child_exploitation", "grooming", "sextortion",
+    "fraud", "scam", "social_engineering", "impersonation",
+    "profanity", "extremism", "political", "war", "espionage", "cybersecurity", "religious", "lgbt",
+    "misinformation", "copyright_violation", "pii_exposure",
+    "none",
+]
+```
+
+</details>
+
+<details>
+<summary><b>Intent</b> — all 13 labels</summary>
+
+Classifies the intent behind a message. Single-label.
+
+| Labels | |
+|--------|--|
+| Benign | `informational` `instructional` `conversational` `persuasive` `creative` `transactional` `emotional_support` `testing` |
+| Ambiguous | `ambiguous` `extractive` |
+| Malicious | `adversarial` `threatening` `solicitation` |
+```python
+INTENT_LABELS = [
+    "informational", "instructional", "conversational", "persuasive",
+    "creative", "transactional", "emotional_support", "testing",
+    "ambiguous", "extractive",
+    "adversarial", "threatening", "solicitation",
+]
+```
+
+</details>
+
+<details>
+<summary><b>Tone of Voice</b> — all 10 labels</summary>
+
+Classifies the tone of a message. Single-label.
+
+| Label | Description |
+|-------|-------------|
+| `neutral` | Matter-of-fact, no strong emotional coloring |
+| `formal` | Professional or official register |
+| `humorous` | Playful, joking, or light-hearted |
+| `sarcastic` | Ironic or mocking tone |
+| `distressed` | Anxious, upset, or overwhelmed |
+| `confused` | Unclear intent, disoriented phrasing |
+| `pleading` | Urgent requests, begging for help or compliance |
+| `aggressive` | Hostile, confrontational, or threatening |
+| `manipulative` | Attempts to exploit, deceive, or coerce |
+| `deceptive` | Deliberately misleading or false framing |
+```python
+TOV_LABELS = [
+    "neutral", "formal", "humorous", "sarcastic",
+    "distressed", "confused", "pleading",
+    "aggressive", "manipulative", "deceptive",
+]
+```
+
+</details>
+</details>