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Student Progress Tracking

Description: Classify student assessment results to monitor their progress and identify areas that require improvement.

How to Use

Here is how to use this model to classify text into different categories:

    from transformers import AutoModelForSequenceClassification, AutoTokenizer
    
    model_name = "interneuronai/student_progress_tracking_bart"
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    def classify_text(text):
        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
        outputs = model(**inputs)
        predictions = outputs.logits.argmax(-1)
        return predictions.item()
    
    text = "Your text here"
    print("Category:", classify_text(text)) 
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Model size
612M params
Tensor type
F32
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