radinrad/WISEST

Model Card for Model ID

WISEST (WhIch Systematic Evidence Synthesis is besT) is an innovative tool designed to streamline the quality assessment of systematic reviews (SRs) using automated deep neural information retrieval techniques. Developed collaboratively by information retrieval specialists and health knowledge synthesis experts, WISEST addresses information overload by automating responses to the widely used ROBIS and AMSTAR-2 frameworks, enhancing the consistency and efficiency of SR evaluation. The user-friendly interface, refined through feedback from active SR practitioners, ensures accessibility for both technical and non-technical users. WISEST’s open-source code base promotes transparency and adaptability, empowering researchers and healthcare professionals to make more reliable, evidence-based decisions with reduced effort and improved accuracy.

You can find the model demo at https://wisest.ls3.rnet.torontomu.ca

You can find data used for fine-tuning at https://wisest-data.ls3.rnet.torontomu.ca

You can find the annotation studio developed for the model at https://github.com/radinhamidi/WISEST-Annotation-Platform

You can find the pipeline implemented for data cleaning and preparation at https://github.com/radinhamidi/WISEST-Data

Model Details

Model Description

• Developed by: Naser Al-Obeidat, Radin Hamidi Rad
• Funded by [optional]: Laboratory for Systems, Software and Semantics: https://ls3.rnet.torontomu.ca
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): English
• License: CC-BY-NC 4.0
• Finetuned from model [optional]: Llama-3.1-8B

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: https://wisest.ls3.rnet.torontomu.ca

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

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

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

• PEFT 0.13.2