README.md

---
library_name: transformers
tags:
- education
datasets:
- NiharMandahas/Os_evaluator
language:
- en
base_model:
- NousResearch/Llama-2-7b-chat-hf
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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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.

- **Developed by:** Nihar Mandahas 
- **Model type:** Pytorch,Finetuned Llama2-7b-chat
- **License:** [More Information Needed]
- **Finetuned from model:** NousResearch/Llama-2-7b-chat-hf

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/PranavDarshan/AutoGrader
- **Paper [optional]:** https://ieeexplore.ieee.org/document/10817016
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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.

Foreseeable Users:

Educators and Examiners – University professors and teachers who assess student responses can leverage the system to expedite grading and maintain consistency.
Students – By ensuring fair and unbiased evaluation, students receive objective feedback, improving their learning experience.
Academic Institutions – Schools and universities can integrate this system into their assessment frameworks, enhancing efficiency in large-scale evaluations.
Affected Stakeholders:

Handwritten Answer Evaluation – The integration of handwriting recognition ensures that students who submit handwritten scripts are evaluated fairly.
Educational Technology Providers – The model can be adopted into existing learning management systems to enhance automated assessment tools.
Policy Makers in Education – Standardized, unbiased grading could influence educational reforms related to assessment methodologies.
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.

By addressing the challenges associated with traditional grading, this model aims to revolutionize the assessment process, making it more efficient, accurate, and fair.


### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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