Update README.md

README.md

@@ -93,65 +93,197 @@ The training demonstrated strong stability and smooth convergence towards sub-0.
 
 ---
 
-## 🧰 Usage Example
+## Usage Example
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import TextStreamer
 
 model_id = "yasserrmd/LFM2-350M-Extract-TOON"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
 
 schema = """
-$schema: http://json-schema.org/draft-07/schema#
+"$schema": "http://json-schema.org/draft-07/schema#"
 type: object
 properties:
-  users:
-    type: array
-    items:
-      type: object
-      properties:
-        id:
-          type: integer
-        name:
-          type: string
-        role:
-          type: string
-          enum: admin, user
-      required: id, name, role
-required: users
+id:
+type: string
+pattern: "^(\\d+\\.\\d+) disturbing$"
+description: Dot-separated integers representing the unique ID of each element in the hierarchy
+title:
+type: string
+description: Descriptive title of the section or element
+level:
+type: integer
+minimum: 0
+maximum: 9
+description: "Hierarchical level (0 - ROOT, 1 - SECTION, 2 - SUBSECTION, 3+ - DETAIL_N)"
+level_type:
+type: string
+enum[4]: ROOT,SECTION,SUBSECTION,DETAIL_N
+description: Type of the hierarchical element
+component:
+type: array
+items:
+type: object
+properties:
+idc:
+type: integer
+description: Component ID
+component_type:
+type: string
+enum[4]: PARAGRAPH,TABLE,CALCULATION,CHECKBOX
+description: Type of component
+metadata:
+type: string
+description: "Additional metadata (e.g., title, note, or overview)"
+properties:
+type: object
+properties:
+variables:
+type: array
+items:
+type: object
+properties:
+idx:
+type: string
+description: Unique row-column identifier (X.Y format)
+name:
+type: string
+description: Attribute name
+value:
+type: string
+description: Attribute value
+unit:
+type[2]: string,"null"
+description: Optional unit for the value
+metrics:
+type: boolean
+description: Boolean flag indicating if the attribute is a metric
+formula:
+type: boolean
+description: Boolean flag indicating if the attribute is a formula
+content:
+type: array
+items:
+type[2]: string,"null"
+description: Text content
+children:
+type: array
+items:
+"$ref": #
+required[6]: id,title,level,level_type,component,children
 """
 text = """
-[2025-11-11 10:34] New account created -> Alice | role: admin | id#1
-[2025-11-11 10:37] User joined -> Bob | regular user | id#2
+SUBSECTION component[1]: - idc: 1 component_type: PARAGRAPH metadata: "<note>Note: Specific to debtor risk.</note>" properties: variables[0]: content[1]: The risk of debtors failing to make payments on time. - id: "2.2" title: Liquidity Risk level: 2 level_type: SUBSECTION component[1]: - idc: 1 component_type: PARAGRAPH metadata: "<note>Note: Specific to liquidity risk.</note>" properties: variables[0]: content[1]: Liquidity risk is related to the difficulty in selling assets quickly without a significant loss.
+
+The document begins with an inclusive overview, elucidating the purpose of the report and its objective to assess risks and propose mitigations for financial operations, such as compliance, fraud detection, and performance metrics. The overall framework is meticulously divided into several sections and subsections reflecting detailed and structured analysis.
+
+This report is intended to provide a comprehensive understanding of risk exposure within financial operations. We will now delve into the first section of the report, which covers a vast array of compliance regulations critical for maintaining financial accountability.
+
+Firstly, let’s examine the **Compliance Section**. The section’s primary aim is to highlight the key compliance regulations applicable to financial operations. Notably, this includes the **Anti-Money Laundering (AML) Regulation (RC.1)** and the **Data Privacy Act (RC.2)**. Highlighting the significance of these regulations, the Subsection on Anti-Money Laundering identifies several gaps within the current system. These gaps need to be addressed to ensure robust compliance. The analysis suggests the presence of several risk points where the current practices might fall short of regulatory standards.
+
+Next, we have a **Detailed Risk Analysis** for the Anti-Money Laundering Regulation. This component outlines the specific risks and potential impacts on financial operations. In the document, a table detailing the risk assessment is provided outlining two primary risks, **Fraudulent Transactions (RA.1)**, and **Non-Compliance with AML (RA.2)**, each with a brief description of the risk and its possible consequences. Addressing these risks requires a systematic approach, ensuring all preventive measures are in place to mitigate financial risks effectively.
+
+Moreover, a **Checklist** is included to assess the current status concerning the Anti-Money Laundering Regulation. The Checklist requires the selection of the best option that describes the current status as either **Option 1 (true)** or **Option 2 (false)**. This selection is pivotal in making informed decisions about regulatory compliance and operational adjustments.
+
+In parallel, the **Data Privacy Act** (RC.2) Subsection identifies several issues in handling personal data. These issues need to be corrected to fully comply with the Data Privacy Act. The **Fraud Detection Section** and its **Subsections on Misrepresentation and Theft of Data** follow a similar structure, detailing the critical risks associated with these vulnerabilities and emphasizing the necessity for mitigation strategies.
+
+In the **Fraud Detection Section**, we have a table outlining two major cases of fraud: **Misrepresentation (FC.1)** and **Theft of Data (FC.2)**. These cases are significant due to their impact on financial integrity and operational continuity. The analysis of these cases includes detailed descriptions of the nature and extent of the fraud, highlighting the importance of robust fraud detection mechanisms.
+
+Each regulatory and fraud-related section is equipped with thorough analysis and checks, ensuring that every risk is identified and addressed. While the sections provide detailed tables and checklists, they also reflect the broader context of financial operations and the mitigation strategies required to ensure compliance and prevent fraud.
+
+By providing these detailed sections and sub-sections, the report aims to equip stakeholders with the necessary information to assess and improve the risk management framework. This ensures that all financial operations are conducted in a compliant, transparent, and secure manner, thereby safeguarding the interests of all stakeholders involved.
+
 """
 
-system = (
-    "You are an intelligent model specialized in converting natural language text "
-    "into valid TOON (Token-Oriented Object Notation) format. "
-    "Always follow the given schema strictly, emit the correct header "
-    "in the form <label>[1]{fields}: followed by exactly one values row. "
-    "Do not include explanations or additional commentary."
+system_instruction = (
+        "You are an intelligent model specialized in converting natural language text"
+        "into valid TOON (Token-Oriented Object Notation) format. "
+        "Always follow the given schema strictly, emit the correct header "
+        "in the form <label>[1]{fields}: followed by exactly one values row. "
+        "Do not include explanations or additional commentary."
+    )
+    
+
+user_prompt = (
+    f'Generate TOON format using the schema {schema} '
+    f'for the below text "{text}".'
 )
-user = f'Use schema: {schema}\nText: "{text}"'
+
 
 messages = [
-    {"role": "system", "content": system},
-    {"role": "user", "content": user}
+    {"role": "system", "content": system_instruction},
+    {"role": "user", "content": user_prompt}
 ]
 
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=80, temperature=0)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt = True, # Must add for generation
+    return_tensors = "pt",
+    tokenize = True,
+    return_dict = True,
+).to("cuda")
+
+
+_ = model.generate(
+    **inputs,
+    max_new_tokens = 2046, # Increase for longer outputs!
+    # Recommended Liquid settings!
+    temperature = 0.3, min_p = 0.15, repetition_penalty = 1.05,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
 ```
 
 **Expected Output:**
 
 ```
-users[2]{id,name,role}:
-  1,Alice,admin
-  2,Bob,user
+id: "0.0"
+title: Financial Risk Assessment Report
+level: 0
+description: Overview of financial risks and mitigation strategies.
+level_type: ROOT
+component[1]:
+  - idc: 1
+    component_type: PARAGRAPH
+    metadata: <note>Note: Specific to debtor risk.</note>"
+    properties:
+      variables[0]:
+      content[1]: The risk of debtors failing to make payments on time.
+children[1]:
+  - id: "1.0"
+    title: Compliance Section
+    level: 1
+    level_type: SECTION
+    component[1]:
+      - idc: 1
+        component_type: PARAGRAPH
+        metadata: <note>Note: Specific to liquidity risk.</note>"
+        properties:
+          variables[0]:
+          content[1]: The risk of liquidity risk is related to the difficulty in selling assets quickly without a significant loss.
+    children[1]:
+      - id: "1.1"
+        title: Detailed Risk Analysis
+        level: 2
+        level_type: SUBSECTION
+        component[1]:
+          - idc: 1
+            component_type: TABLE
+            metadata: <note>Table of Risks</note>"
+            properties:
+              variables[2]{idx,name,value,unit,metrics}:
+                "0.0",Risk Assessment,false,null,false
+                "0.1",Risks,Fraudulent Transactions,null,false
+              content[1]: Fraudulent Transactions (RA.1), Non-Compliance with AML,null,false
+          - idc: 2
+            component_type: CHECKBOX
+            metadata: <note>Checklist for compliance</note>
+            properties:
+              variables[0]:
+              content[1]: Option 1 (true),Option 2 (false)<|im_end|>
 ```
 
 ---
@@ -167,7 +299,7 @@ users[2]{id,name,role}:
 
 ---
 
-## 🚀 Intended Use
+## Intended Use
 
 * **Structured data extraction** from unstructured text.
 * **Compact schema-based representations** for LLM pipelines.
@@ -205,7 +337,7 @@ users[2]{id,name,role}:
 
 ---
 
-## 🙏 Acknowledgements
+## Acknowledgements
 
 * **Base model:** LiquidAI team for LFM2-350M-Extract
 * **Fine-tuning framework:** Unsloth AI
@@ -214,7 +346,7 @@ users[2]{id,name,role}:
 
 ---
 
-## 📜 Version History
+## Version History
 
 | Version | Date       | Changes                                  |
 | ------- | ---------- | ---------------------------------------- |