Text Classification
Transformers
Safetensors
PEFT
English
roberta
nlp
lora
multitask-learning
customer-support
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("San-Analytics/TicketIQ-MultiTask")
model = AutoModelForSequenceClassification.from_pretrained("San-Analytics/TicketIQ-MultiTask")Quick Links
TicketIQ-MultiTask
TicketIQ is a multi-task NLP model for automated customer support ticket triage.
A single RoBERTa encoder fine-tuned with LoRA predicts:
- Ticket Category
- Ticket Priority
- Customer Sentiment
All predictions are produced in a single forward pass.
Model Overview
| Task | Example Labels |
|---|---|
| Category | account, billing, technical, shipping |
| Priority | low, medium, high, critical |
| Sentiment | positive, neutral, negative |
Architecture:
RoBERTa Base
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Shared Encoder
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Category
Priority
Sentiment
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="San-Analytics/TicketIQ-MultiTask")