distilbert-course-review-classification

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:

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/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:

# 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:

# 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 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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3. **Keep the document updated** as the model evolves or more information becomes available.