Text Classification
Transformers
Safetensors
English
distilbert
urgency-detection
startup-grievances
huggingface
text-embeddings-inference
Instructions to use KS-Vijay/urgency-model-aura with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KS-Vijay/urgency-model-aura with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KS-Vijay/urgency-model-aura")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KS-Vijay/urgency-model-aura") model = AutoModelForSequenceClassification.from_pretrained("KS-Vijay/urgency-model-aura") - Notebooks
- Google Colab
- Kaggle
π¨ Urgency Model Aura (KS-Vijay)
This is a DistilBERT-based text classification model trained to detect the urgency level of textual grievances submitted by startups. It's part of an AI-based Grievance Redressal Platform for startups.
π§ Use Case
Helps categorize complaints into urgency levels:
LowMediumHighCritical
This allows startups or organizations to prioritize tickets and respond efficiently based on severity.
π How It Works
The model takes in a complaint text and outputs a classification label. It was trained on labeled grievance data using the π€ transformers and datasets libraries.
π¦ Model Details
- Architecture:
DistilBERT - Framework:
PyTorch - Model type:
Text Classification - Format:
safetensors - Dataset: Custom
complaints.csv(internal) - Labels: Urgency levels (Critical, High, Medium, Low)
π Example Input
"My startup's payment system has been offline since morning!"
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Model tree for KS-Vijay/urgency-model-aura
Base model
distilbert/distilbert-base-uncased