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1pip install transformers
2pip install torch # or tensorflow depending on your preference1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2
3tokenizer = AutoTokenizer.from_pretrained("username/distilbert-course-review-classification")
4model = AutoModelForSequenceClassification.from_pretrained("username/distilbert-course-review-classification")
5
6# Example usage
7review = "The course content is great, but I would like more examples."
8
9inputs = tokenizer(review, return_tensors="pt", padding=True, truncation=True)
10outputs = model(**inputs)
11
12# Assuming the model outputs logits
13predicted_class = outputs.logits.argmax(dim=-1).item()
14
15class_labels = [
16 'Improvement Suggestions', 'Questions', 'Confusion', 'Support Request',
17 'Discussion', 'Course Comparison', 'Related Course Suggestions',
18 'Negative', 'Positive'
19]
20
21print(f"Predicted class: {class_labels[predicted_class]}")1# Example inference
2review = "I found the course material very confusing and hard to follow."
3
4inputs = tokenizer(review, return_tensors="pt", padding=True, truncation=True)
5outputs = model(**inputs)
6
7# Assuming the model outputs logits
8predicted_class = outputs.logits.argmax(dim=-1).item()
9
10class_labels = [
11 'Improvement Suggestions', 'Questions', 'Confusion', 'Support Request',
12 'Discussion', 'Course Comparison', 'Related Course Suggestions',
13 'Negative', 'Positive'
14]
15
16print(f"Predicted class: {class_labels[predicted_class]}")1# Example training code
2from transformers import Trainer, TrainingArguments
3
4training_args = TrainingArguments(
5 output_dir="./results",
6 evaluation_strategy="epoch",
7 per_device_train_batch_size=8,
8 per_device_eval_batch_size=8,
9 num_train_epochs=3,
10 weight_decay=0.01,
11)
12
13trainer = Trainer(
14 model=model,
15 args=training_args,
16 train_dataset=train_dataset,
17 eval_dataset=eval_dataset,
18)
19
20trainer.train()
### 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.