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Gym Membership Upgrades

Description: Classify member feedback to identify potential areas of improvement and opportunities for upselling premium services, such as personal training or nutrition counseling.

How to Use

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

    from transformers import AutoModelForSequenceClassification, AutoTokenizer
    
    model_name = "interneuronai/gym_membership_upgrades_pegasus"
    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
192M params
Tensor type
F32
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