README.md

---
library_name: transformers
tags:
- unsloth
---

# Model Card for Model ID

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Fine-tuned Mistral-7b specifically for evaluating IELTS essays with Direct Preference Optimization (DPO)


## Model Details

### Model Description

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 The model was fine-tuned using the QLoRA technique on a dataset of over 9,000 IELTS essay samples. QLoRA is a low-rank adaptation method that enables efficient fine-tuning of large language models while minimizing memory and compute requirements. The model was trained to predict essay scores based on the IELTS scoring rubric, which evaluates essays on four criteria: task achievement, coherence and cohesion, lexical resource, and grammatical range and accuracy. The model has been tested on a held-out set of essays and achieves strong performance, making it a useful tool for automated essay evaluation. To use the model, simply provide it with the text of an essay and it will output a predicted score between 0 and 9 for each of the four scoring criteria.

Uses: This model can be used by educators and students to evaluate the quality of IELTS essays and provide feedback on areas for improvement. It can also be used by test preparation companies to automatically score practice essays and provide students with instant feedback.

Limitations: While the model has been trained on a large dataset of IELTS essays, it may not perform as well on essays that are significantly different from those in the training set. Additionally, the model may not fully capture the nuances of human language and may occasionally make errors in scoring. It is recommended that the model be used as a tool to supplement human evaluation rather than as a replacement for it.

We hope you find this model useful for your IELTS essay evaluation needs!

- **Developed by:** Nguyen Minh Chi
- **Funded by [optional]:** Unsloth
- **Shared by [optional]:** [More Information Needed]
- **Model type:** 4-bit
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Mistral-7B

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### 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.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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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- **Hours used:** [More Information Needed]
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## 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]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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