README.md

---
datasets:
- chillies/course-review-multilabel-sentiment-analysis
language:
- en
metrics:
- accuracy
- f1
library_name: transformers
---

# distilbert-course-review-classification

[![Model Card](https://img.shields.io/badge/Hugging%20Face-Model%20Card-blue)](https://huggingface.co/username/distilbert-course-review-classification)

## Description

**distilbert-course-review-classification** is a fine-tuned version of DistilBERT, specifically trained for sentiment analysis of online course reviews. This model categorizes reviews into the following classes:
- Improvement Suggestions
- Questions
- Confusion
- Support Request
- Discussion
- Course Comparison
- Related Course Suggestions
- Negative
- Positive

## 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/distilbert-course-review-classification")
model = AutoModelForSequenceClassification.from_pretrained("username/distilbert-course-review-classification")

# Example usage
review = "The course content is great, but I would like more examples."

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

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

class_labels = [
    'Improvement Suggestions', 'Questions', 'Confusion', 'Support Request',
    'Discussion', 'Course Comparison', 'Related Course Suggestions',
    'Negative', 'Positive'
]

print(f"Predicted class: {class_labels[predicted_class]}")
```

### Inference

Provide example code for performing inference with your model:

```python
# Example inference
review = "I found the course material very confusing and hard to follow."

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

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

class_labels = [
    'Improvement Suggestions', 'Questions', 'Confusion', 'Support Request',
    'Discussion', 'Course Comparison', 'Related Course Suggestions',
    'Negative', 'Positive'
]

print(f"Predicted class: {class_labels[predicted_class]}")
```

### 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 online course reviews, labeled with the following sentiment categories:
- Improvement Suggestions
- Questions
- Confusion
- Support Request
- Discussion
- Course Comparison
- Related Course Suggestions
- Negative
- Positive

### Training Procedure

The model was fine-tuned using a standard training approach, optimizing for accurate sentiment classification. 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 distilbert-course-review-classification was benchmarked against other sentiment analysis models, demonstrating superior accuracy and relevance in classifying online course reviews.

## Limitations and Biases

While distilbert-course-review-classification is highly effective, it may have limitations in the following areas:
- It may not fully understand the context of complex reviews.
- There may be biases present in the training data that could affect the classification results.

## 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.