Instructions to use interneuronai/it_support_ticket_classification_pegasus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/it_support_ticket_classification_pegasus with Transformers:
# 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") - Notebooks
- Google Colab
- Kaggle
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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