Model Card for VeriQ

Important Notice: This Model Card describes the planned VeriQ model, which is currently under active development. Features, architecture, performance metrics, training details, and evaluation results described herein represent the intended design and goals. As the model is not yet finalized, specific operational data is To Be Determined (TBD) and subject to change. The model is not yet available for use or inference.

VeriQ is envisioned as a multimodal Artificial Intelligence system designed for anomaly detection, classification, and explanation across diverse data sources including visual (images, video), textual (code, documents), and tabular (metrics, telemetry) inputs. Its primary goal will be to enhance quality governance and prevent failures by identifying potential issues before they impact production environments across various industries.

Model Details

Model Description

VeriQ is designed to integrate Computer Vision, Natural Language Processing (NLP), and Tabular Data Analysis into a single multimodal engine. It is planned to leverage specialized encoders for each modality (e.g., Swin Transformer, EfficientNetV2-L for vision; RoBERTa-large for text; Attention-based encoders for tabular data - Planned Components) and employ a cross-modal fusion module (e.g., Multi-Head Self-Attention - Planned Mechanism) to create unified representations. A classifier ensemble (Planned Architecture, potentially including MLPs, GBDTs, Transformer heads) will perform the final anomaly detection (Yes/No or Category) and severity score regression (0-1). A key planned feature is integrated Explainability (XAI) using techniques like SHAP and attention visualization (Planned Techniques) to highlight the evidence behind its predictions. The system is designed with a cloud-native architecture for scalability and maintainability.

Target applications are envisioned to span Software Engineering, FinTech, Manufacturing 4.0, Energy & Utilities, HealthTech, Telecommunications, E-commerce, Precision Agriculture, LegalTech, and Government/Critical Infrastructure.

Developed by: Emanuel Lázaro Custódio Silva
Model type: Multimodal Fusion Network (Planned Architecture - incorporating Vision Transformers, Text Transformers, Tabular Attention Encoders, MHSA Fusion, and Classifier Ensembles)
Language(s) (NLP): Multilingual (Planned Capability via RoBERTa-large fine-tuned encoder). Specific language performance TBD. Potential primary languages: en, pt.
License: bigscience-openrail-m (Intended License)
Finetuned from model: Not applicable (custom planned architecture using pre-trained components like RoBERTa, Swin Transformer, EfficientNetV2-L).

Model Sources

Repository: Hugging Face
Paper: [Link to Paper - TBD, will be provided if a publication occurs]
Demo: [Link to SaaS Platform/Demo - TBD, planned for future release]

Uses

Direct Use

The model is intended to be used via its planned API (REST, gRPC, WebSocket), Python SDK, CLI, or a dedicated Web Interface (SaaS platform). Users (e.g., developers, QA engineers, security analysts, financial analysts, auditors, domain experts) will potentially be able to submit multimodal data (images/video, text snippets, tabular metrics) to receive:

Anomaly detection status (Yes/No or category like 'security', 'performance').
A numerical severity score (0.0-1.0) for prioritization.
Explainability outputs highlighting the input features contributing to the prediction.

This would allow for direct analysis of code quality, transaction fraudulence, visual inspection results, compliance checks, etc., once the model is developed and deployed.

Downstream Use

VeriQ's outputs could potentially be integrated into larger systems and workflows once available:

CI/CD Pipelines: Automatically flag potentially problematic code commits.
Monitoring Systems: Enhance existing monitoring with multimodal anomaly detection (e.g., correlating sensor data with inspection images).
Fraud Detection Platforms: Add another layer of analysis to financial transaction systems.
Automated Reporting: Generate quality or compliance reports incorporating VeriQ's findings.
Ticketing Systems: Automatically create tickets (e.g., Jira, ServiceNow) based on detected high-severity anomalies.

Out-of-Scope Use

Uses explicitly prohibited by the intended BigScience OpenRAIL-M license (refer to Schedule A of the license text) are planned to be out-of-scope. This typically includes, but is not limited to: generating misinformation, non-consensual surveillance, violating laws, discriminating against protected groups, impersonation, and applications in sensitive areas like weaponry or critical infrastructure control without appropriate safeguards.

Additionally, the model should not be used for (these represent intended limitations):

Tasks other than anomaly detection, classification, and severity scoring based on the input modalities.
Making high-stakes decisions without human oversight and validation.
Analyzing data types or domains significantly different from those it will be trained on, as performance may degrade unpredictably.
Generating misleading reports or manipulating findings based on its output.
Attempting to reverse-engineer sensitive information from the model or its explanations beyond intended use.

Bias, Risks, and Limitations

Note: The following describes anticipated biases, risks, and limitations based on the planned model design and potential data sources. Actual characteristics will be determined after development and rigorous evaluation.

Potential Bias: The model's performance and predictions could potentially reflect biases present in the eventual training data. This could include skew towards specific types of software defects, demographic biases in financial or medical data, or biases inherent in the pre-trained encoder models (e.g., RoBERTa). Biases could lead to disparate performance across different subgroups or domains. [Specific bias analysis TBD upon evaluation].
Anticipated Risks:
- False Negatives: Failing to detect critical anomalies could lead to significant failures (security breaches, financial loss, safety issues).
- False Positives: Incorrectly flagging normal instances as anomalies can lead to alert fatigue and wasted resources.
- Over-Reliance: Users might trust the model's output without necessary human verification, especially in critical applications.
- Misinterpretation of Explanations: XAI methods show feature importance/correlation, not necessarily causation, which could be misinterpreted.
- Security/Privacy: Handling sensitive data (code, financial records, medical images) requires robust security. Explanations could potentially reveal sensitive patterns if not handled carefully.
Anticipated Limitations:
- Performance will be dependent on the quality and representativeness of the training data.
- May struggle with entirely novel or out-of-distribution anomaly types not seen during training.
- The effectiveness of XAI will depend on the specific methods used and the complexity of the model.
- Computational cost for inference might be significant due to the multimodal architecture.
- Scalability and throughput limits TBD.

Recommendations

Users (both direct and downstream) should eventually be made aware of the risks, biases and limitations of the model once identified through rigorous evaluation.

Human-in-the-Loop: Critical decisions should always involve human review and validation of the model's outputs.
Domain Expertise: Interpret results in the context of specific domain knowledge.
Data Quality: Ensure input data quality is high for reliable predictions.
Monitor Performance: Continuously monitor the model's performance in production for drift or degradation once deployed.
Evaluate Fairness: Assess model performance across relevant subgroups if applicable to the use case during evaluation.
Use Explanations Critically: Treat XAI outputs as diagnostic aids, not definitive causal proof.
Adhere to License Restrictions: Strictly follow the use limitations outlined in the OpenRAIL-M license.

How to Get Started with the Model

Note: The model is currently under development and not yet available for use. The following usage examples are hypothetical and conceptual to illustrate the intended usage pattern once the model is developed, deployed, and documented. This code will not run currently.

Python SDK (Hypothetical):

# HYPOTHETICAL USAGE EXAMPLE - MODEL AND SDK ARE UNDER DEVELOPMENT
from veriq import Client # Assuming a future 'veriq' client library

# Client initialization will require endpoint and credentials (TBD)
client = Client(api_url="YOUR_FUTURE_API_ENDPOINT", api_key="YOUR_FUTURE_API_KEY")

# Example data (replace with actual data loading)
image_bytes = b"..." # Bytes from an image file
text_input = "Code comment: Fixed potential null pointer exception."
metrics_data = {"cyclomatic_complexity": 12, "lines_of_code": 350}

# Perform multimodal inference (API call structure TBD)
response = client.infer(
    image=image_bytes,
    text=text_input,
    metrics=metrics_data
)

# Process results (response object structure TBD)
print(f"Anomaly Detected: {response.anomaly}")
print(f"Severity Score: {response.severity_score:.2f}")
print(f"Explanation: {response.explanation}")

API (Conceptual POST Request):

# CONCEPTUAL EXAMPLE - API ENDPOINT AND PAYLOAD TBD
POST /infer
Authorization: Bearer <YOUR_FUTURE_API_KEY>
Content-Type: application/json

{
  "image": "<base64_encoded_image_bytes>",
  "text": "Relevant text snippet...",
  "metrics": {
    "cyclomatic_complexity": 15,
    "lines_of_code": 500,
    "sensor_temp": 45.5
  }
}

(Refer to official documentation once available for detailed instructions and functional code examples)

Training Details

Note: The following training details describe the planned approach. Specific datasets, procedures, hyperparameters, and results are To Be Determined (TBD) during the development and evaluation phases.

Training Data

[Training data details TBD. Expected to be a diverse, curated collection of multimodal datasets representing various target domains (software engineering, finance, manufacturing, healthcare, legal, etc.). Data sources may include proprietary datasets, public benchmarks adapted for multimodal use, and potentially synthetic data. Specific dataset cards will be linked when available. Data preprocessing steps are outlined below.]

Training Procedure

Preprocessing

Input data will undergo modality-specific preprocessing before being fed to the encoders:

Visual: Images and video frames will be decoded, potentially resized/cropped, normalized, and converted into tensor formats suitable for models like Swin Transformer/EfficientNetV2-L. ASTs/Call Graphs will likely be rendered as images.
Textual: Text data (comments, docs, OCR output) will be tokenized using a specific tokenizer (e.g., RoBERTa's), normalized (lowercase, punctuation removal if applicable), and converted into input IDs and attention masks.
Tabular: Numerical metrics will be scaled/normalized (e.g., Z-score, Min-Max scaling). Categorical features (if any) will be encoded. Time series data might undergo specific windowing or feature engineering.

Training Hyperparameters

Training regime: [Details TBD. Likely mixed-precision training (e.g., fp16 or bf16) for efficiency on suitable hardware.]
Other hyperparameters (learning rate, batch size, optimizer, epochs, etc.) TBD.

Speeds, Sizes, Times

[Training time, speeds, and final model checkpoint sizes TBD and will be documented after training.]

Evaluation

Note: Evaluation protocols and results are To Be Determined (TBD) and will be conducted rigorously once the model is trained and tested.

Testing Data, Factors & Metrics

Testing Data

[Details TBD. Evaluation will use held-out test sets, carefully curated to mirror the diversity of expected real-world data across different domains and modalities. Public benchmarks may be used for specific modality components where applicable.]

Factors

[Evaluation results will ideally be disaggregated by factors such as:

Input Modality (performance on text-only vs. image-only vs. multimodal inputs)
Target Domain (e.g., Software vs. Finance vs. Medical)
Anomaly Type/Category
Anomaly Severity Level]

Metrics

[Primary evaluation metrics will likely include:

Classification: Accuracy, Precision, Recall, F1-Score (weighted/macro/micro), Confusion Matrix.
Ranking/Detection: Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PR).
Regression (Severity Score): Mean Squared Error (MSE), Mean Absolute Error (MAE).
XAI: Metrics for faithfulness or plausibility might be explored (TBD).]

Results

[Quantitative evaluation results TBD and will be published here after model evaluation.]

Summary

[An overall summary of the model's performance based on evaluation results TBD.]

Model Examination

Interpretability is a core design goal (Planned Approach), intended to be addressed via integrated XAI components (SHAP, Attention Visualization). These methods will be used during development and potentially exposed to end-users to understand which input features drive specific predictions. [Further examination details and specific findings TBD.]

Environmental Impact

Note: Environmental impact details are To Be Determined (TBD) and will be estimated and reported after training is complete, based on the actual hardware and time used.

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [Details TBD - Anticipated to be high-performance GPU servers, e.g., NVIDIA A100/H100 or similar]
Hours used: [Details TBD]
Cloud Provider: [Details TBD - e.g., GCP, AWS, Azure, or other]
Compute Region: [Details TBD]
Carbon Emitted: [Estimate TBD]

Technical Specifications

Note: Technical specifications describe the planned architecture and infrastructure. Implementation details are subject to change during development.

Model Architecture and Objective

The model is planned to employ a multimodal architecture featuring:

Parallel modality-specific encoders (Planned Components: Vision: Swin Transformer + EfficientNetV2-L; Text: RoBERTa-large fine-tuned; Tabular: Attention-based Tabular Encoder).
A Cross-Modal Fusion module (Planned Mechanism: Multi-Head Self-Attention) to integrate encoded representations.
A Classifier Ensemble (Planned Architecture: MLPs, GBDTs, Transformer heads) for final prediction.
Integrated XAI components (Planned Techniques: SHAP, Attention Visualization). The planned training objective is likely a composite loss function minimizing classification error (e.g., cross-entropy for anomaly type) and regression error (e.g., MSE for severity score).

Architecture Diagram

Compute Infrastructure

Hardware

Training: [Details TBD - Anticipated to require high-performance GPU servers (e.g., NVIDIA A100s, H100s or equivalent). Access to TPUs is also a possibility.]
Inference: [Details TBD - Likely scalable GPU instances (e.g., NVIDIA T4, L4, A10G) managed via Kubernetes (GKE mentioned as a possibility) for SaaS. Edge hardware (ARM/Jetson) compatibility is a future goal.]

Software

Core Framework: [Details TBD - Python with PyTorch or TensorFlow as the main deep learning framework.]
Key Libraries: [Details TBD - Likely Hugging Face transformers, scikit-learn, pandas, shap, OpenCV, Docker, Kubernetes (GKE), potentially Kafka/Pulsar, API Gateway solutions, Object Storage, Databases (SQL/NoSQL/Redis), Monitoring tools (OpenTelemetry, Prometheus, Grafana), IaC tools (Terraform, Helm).]

Citation

Note: A formal citation will be provided if and when a relevant paper, technical report, or blog post detailing the VeriQ model is published. Please use the repository link for attribution in the meantime as this model is under development.

BibTeX (Placeholder):

@misc{veriq_model_tbd,
  author       = {Emanuel Lázaro Custódio Silva},
  title        = {VeriQ: A Planned Multimodal Model for Anomaly Detection and Quality Governance},
  year         = {2025},
  publisher    = {Hugging Face},
  journal      = {Hugging Face Hub},
  howpublished = {\url{https://www.huggingface.co/emanuellcs/VeriQ}}
}

APA:

Silva, E. L. C. (2025). VeriQ: A Planned Multimodal Model for Anomaly Detection and Quality Governance. Hugging Face Hub. https://www.huggingface.co/emanuellcs/VeriQ

Glossary

Multimodal: Processing and integrating information from multiple data types (e.g., text, image, tabular).
Anomaly Detection: Identifying instances or patterns that deviate significantly from the norm.
XAI (Explainable AI): Methods and techniques used to understand and interpret the predictions made by AI models.
AST (Abstract Syntax Tree): A tree representation of the syntactic structure of source code.
MHSA (Multi-Head Self-Attention): A mechanism used in Transformer models to weigh the importance of different parts of the input sequence(s) relative to each other.
Severity Score: A numerical output (e.g., 0-1) indicating the predicted seriousness or risk level of a detected anomaly.
RoBERTa: A variant of the BERT language model (Robustly Optimized BERT Pretraining Approach).
Swin Transformer: A hierarchical Vision Transformer using shifted windows.
EfficientNetV2: An iteration of convolutional neural networks known for efficiency and accuracy.
GBDT (Gradient Boosted Decision Trees): An ensemble machine learning technique using decision trees.
MLP (Multi-Layer Perceptron): A type of feedforward artificial neural network.
OpenRAIL-M: A family of Responsible AI Licenses specifically for Models, including use restrictions.
TBD: To Be Determined. Indicates information that will be finalized during or after development.

More Information

[Links to project website, detailed documentation, blog posts, etc. TBD and will be added as they become available.]

Model Card Authors

Emanuel Lázaro Custódio Silva

Model Card Contact

[email protected]