Instructions to use interneuronai/customer_support_ticket_classification_pegasus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/customer_support_ticket_classification_pegasus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/customer_support_ticket_classification_pegasus")# 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") - Notebooks
- Google Colab
- Kaggle
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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