license: apache-2.0
datasets:
- chillies/IELTS-writing-task-2-evaluation
language:
- en
metrics:
- bleu
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:
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:
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:
# 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:
# 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 for more information on how to contribute to this project.
License
This model is licensed under the MIT 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
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