Instructions to use interneuronai/companyx_customer_support_ticket_routing_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/companyx_customer_support_ticket_routing_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/companyx_customer_support_ticket_routing_distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/companyx_customer_support_ticket_routing_distilbert") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/companyx_customer_support_ticket_routing_distilbert") - Notebooks
- Google Colab
- Kaggle
CompanyX Customer Support Ticket Routing
Description: Automatically route customer support tickets to relevant teams based on issue descriptions, speeding up resolution time and enhancing customer experience.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/companyx_customer_support_ticket_routing_distilbert"
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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