How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="interneuronai/it_support_ticket_classification_pegasus")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("interneuronai/it_support_ticket_classification_pegasus")
model = AutoModelForSequenceClassification.from_pretrained("interneuronai/it_support_ticket_classification_pegasus")
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IT Support Ticket Classification

Description: Automatically categorize and prioritize IT support tickets based on their text descriptions, enabling more efficient resolution and customer support.

How to Use

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

    from transformers import AutoModelForSequenceClassification, AutoTokenizer
    
    model_name = "interneuronai/it_support_ticket_classification_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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