Text Classification
Transformers
Safetensors
PEFT
English
roberta
nlp
lora
multitask-learning
customer-support
text-embeddings-inference
Instructions to use San-Analytics/TicketIQ-MultiTask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use San-Analytics/TicketIQ-MultiTask with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="San-Analytics/TicketIQ-MultiTask")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("San-Analytics/TicketIQ-MultiTask") model = AutoModelForSequenceClassification.from_pretrained("San-Analytics/TicketIQ-MultiTask") - PEFT
How to use San-Analytics/TicketIQ-MultiTask with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 815 Bytes
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language: en
license: mit
pipeline_tag: text-classification
tags:
- nlp
- transformers
- roberta
- peft
- lora
- multitask-learning
- customer-support
---
# 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:
```text
RoBERTa Base
│
▼
Shared Encoder
│
┌────┼────┐
▼ ▼ ▼
Category
Priority
Sentiment
Heads
|