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---
license: apache-2.0
datasets:
- chillies/IELTS-writing-task-2-evaluation
language:
- en
metrics:
- bleu
---
# mistral-7b-ielts-evaluator
[](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. |