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--- |
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library_name: transformers |
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tags: |
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- education |
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datasets: |
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- NiharMandahas/Os_evaluator |
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language: |
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- en |
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base_model: |
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- NousResearch/Llama-2-7b-chat-hf |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Nihar Mandahas |
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- **Model type:** Pytorch,Finetuned Llama2-7b-chat |
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- **License:** [More Information Needed] |
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- **Finetuned from model:** NousResearch/Llama-2-7b-chat-hf |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/PranavDarshan/AutoGrader |
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- **Paper [optional]:** https://ieeexplore.ieee.org/document/10817016 |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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The model developed in this study is designed to assist in the automated evaluation of answer scripts, specifically within the domain of operating systems. It aims to streamline the grading process by reducing the time required for evaluation and eliminating human bias. |
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Foreseeable Users: |
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Educators and Examiners – University professors and teachers who assess student responses can leverage the system to expedite grading and maintain consistency. |
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Students – By ensuring fair and unbiased evaluation, students receive objective feedback, improving their learning experience. |
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Academic Institutions – Schools and universities can integrate this system into their assessment frameworks, enhancing efficiency in large-scale evaluations. |
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Affected Stakeholders: |
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Handwritten Answer Evaluation – The integration of handwriting recognition ensures that students who submit handwritten scripts are evaluated fairly. |
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Educational Technology Providers – The model can be adopted into existing learning management systems to enhance automated assessment tools. |
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Policy Makers in Education – Standardized, unbiased grading could influence educational reforms related to assessment methodologies. |
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The model operates by utilizing a fine-tuned Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) to fetch contextual information from prescribed textbooks. Additionally, it integrates handwriting recognition for evaluating manually written answer scripts. The entire system is deployed on an interactive web platform using AWS SageMaker, ensuring scalability and accessibility. |
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By addressing the challenges associated with traditional grading, this model aims to revolutionize the assessment process, making it more efficient, accurate, and fair. |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
