Create README.md

README.md

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+---
+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.