Gemma 4 E2B IT – Regulatory Obligation Extraction v1
Overview
Gemma 4 E2B IT – Regulatory Obligation Extraction v1 is a domain-adapted Small Language Model (SLM) fine-tuned for extracting regulatory obligations from legal and compliance documents.
The model identifies regulatory obligations and converts unstructured regulatory text into structured JSON suitable for downstream Governance, Risk, and Compliance (GRC) applications.
Unlike a general-purpose language model, this model has been optimized specifically for compliance automation workflows and legal NLP tasks involving regulatory documents.
Features
Fine-tuned using QLoRA (4-bit NF4)
Built on Google Gemma 4 E2B IT
Instruction tuned for Legal NLP
Structured JSON generation
Regulatory obligation extraction
Modality classification
Span-level obligation extraction
Optimized for low-memory inference
MLflow experiment tracking
PEFT compatible
Supported Tasks
The model classifies regulatory text into one of three categories:
Obligation
Non-Obligation
Neutral Statement
For obligation statements, the model extracts structured information including:
Subject
Action Required
Modality
Conditions
Deadlines
Reference metadata
Intended Applications
This model is designed for:
Regulatory obligation extraction
Compliance monitoring
Legal document parsing
Governance Risk & Compliance (GRC)
Regulatory change management
Knowledge Graph construction
Retrieval-Augmented Generation (RAG)
Regulatory intelligence
Legal NLP research
Compliance automation pipelines
Not Intended For
This model should not be used as a substitute for legal professionals.
It should not be used for:
Legal advice
Contract drafting
Legal interpretation
Court proceedings
Regulatory opinions
High-risk compliance decisions without human review
Base Model
Property
Value
Base Model
google/gemma-4-E2B-it
Architecture
Gemma 4
Fine-tuning Method
QLoRA
Quantization
4-bit NF4
Framework
Unsloth
Library
Hugging Face Transformers
Adapter Framework
PEFT
Training Configuration
Parameter
Value
Context Length
1024
LoRA Rank (r)
16
LoRA Alpha
16
LoRA Dropout
0.05
Learning Rate
1e-4
Epochs
5
Batch Size
1
Gradient Accumulation
4
Weight Decay
0.01
Scheduler
Cosine
Optimizer
AdamW 8-bit
Gradient Checkpointing
Unsloth
Mixed Precision
FP16
Training Environment
Google Colab
NVIDIA T4 GPU (16 GB VRAM)
Unsloth
Transformers
PEFT
TRL
Accelerate
BitsAndBytes
MLflow
Training Dataset
The model was instruction-tuned using a custom dataset containing regulatory and compliance text collected from publicly available standards and regulations.
Regulatory source: few documents from RBI Guidelines
The training dataset contains examples of:
Positive obligations
Negative (non-obligation) statements
Neutral informational statements
Output Format
The model generates structured JSON.
Example Input
Every financial institution shall maintain customer records for five years.
