# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("interneuronai/customer_support_ticket_classification_pegasus")
model = AutoModelForSequenceClassification.from_pretrained("interneuronai/customer_support_ticket_classification_pegasus")Quick Links
Customer Support Ticket Classification
Description: Categorize customer support tickets based on their content to improve the efficiency of the support team and provide faster resolution times.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/customer_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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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/customer_support_ticket_classification_pegasus")