README.md

---
license: apache-2.0
datasets:
- chillies/IELTS-writing-task-2-evaluation
language:
- en
metrics:
- bleu
---

# mistral-7b-ielts-evaluator

[![Model Card](https://img.shields.io/badge/Hugging%20Face-Model%20Card-blue)](https://huggingface.co/username/mistral-7b-ielts-evaluator)

## Description

**mistral-7b-ielts-evaluator** is a fine-tuned version of Mistral 7B, specifically trained for evaluating IELTS Writing Task 2 essays. This model provides detailed feedback and scoring for IELTS essays, helping students improve their writing skills.

## Installation

To use this model, you will need to install the following dependencies:

```bash
pip install transformers
pip install torch  # or tensorflow depending on your preference
```

## Usage

Here is how you can load and use the model in your code:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("username/mistral-7b-ielts-evaluator")
model = AutoModelForSequenceClassification.from_pretrained("username/mistral-7b-ielts-evaluator")

# Example usage
essay = "Some people believe that it is better to live in a city while others argue that living in the countryside is preferable. Discuss both views and give your own opinion."

inputs = tokenizer(essay, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)

# Assuming the model outputs a score
score = outputs.logits.argmax(dim=-1).item()

print(f"IELTS Task 2 Evaluation Score: {score}")
```

### Inference

Provide example code for performing inference with your model:

```python
# Example inference
essay = "Some people believe that it is better to live in a city while others argue that living in the countryside is preferable. Discuss both views and give your own opinion."

inputs = tokenizer(essay, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)

# Assuming the model outputs a score
score = outputs.logits.argmax(dim=-1).item()

print(f"IELTS Task 2 Evaluation Score: {score}")
```

### Training

If your model can be trained further, provide instructions for training:

```python
# Example training code
from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()
```

## Training Details

### Training Data

The model was fine-tuned on a dataset of IELTS Writing Task 2 essays, which includes a diverse range of topics and responses. The dataset is labeled with scores and feedback to train the model effectively.

### Training Procedure

The model was fine-tuned using a standard training approach, optimizing for accurate scoring and feedback generation. Training was conducted on [describe hardware, e.g., GPUs, TPUs] over [number of epochs] epochs with [any relevant hyperparameters].

## Evaluation

### Metrics

The model was evaluated using the following metrics:

- **Accuracy**: X%
- **Precision**: Y%
- **Recall**: Z%
- **F1 Score**: W%

### Comparison

The performance of mistral-7b-ielts-evaluator was benchmarked against other essay evaluation models, demonstrating superior accuracy and feedback quality in the IELTS Writing Task 2 domain.

## Limitations and Biases

While mistral-7b-ielts-evaluator is highly effective, it may have limitations in the following areas:
- It may not capture the full complexity of human scoring.
- There may be biases present in the training data that could affect responses.

## How to Contribute

We welcome contributions! Please see our [contributing guidelines](link_to_contributing_guidelines) for more information on how to contribute to this project.

## License

This model is licensed under the [MIT License](LICENSE).

## Acknowledgements

We would like to thank the contributors and the creators of the datasets used for training this model.
```

### Tips for Completing the Template

1. **Replace placeholders** (like `username`, `training data`, `evaluation metrics`) with your actual data.
2. **Include any additional information** specific to your model or training process.
3. **Keep the document updated** as the model evolves or more information becomes available.