🧠Insurance LOB Classification Model
Model Details
Model Description
This model is a fine-tuned transformer model for multi-class classification of Insurance Line of Business (LOB) from textual data.
The model is designed for insurance, legal, and underwriting workflows where documents need to be automatically categorized.
- Developed by: Shubham kumar
- Model type: Transformer-based sequence classification
- Language(s): English
- License: Apache 2.0
- Finetuned from: distilbert-base-uncased
Intended Use
Direct Use
- Classify insurance-related text into LOB categories
- Automate document routing in underwriting pipelines
- Assist in triaging insurance submissions
Downstream Use
- Integrated into APIs for real-time classification
- Used in batch pipelines for document processing
- Combined with OCR systems for PDF ingestion
Out-of-Scope Use
- Not suitable for:
- Non-insurance domains
- Non-English text
- Highly ambiguous or very short text
- Legal decision-making without human review
Bias, Risks, and Limitations
- Model performance depends heavily on training data quality
- May be biased toward dominant classes if dataset is imbalanced
- Predictions with low confidence should be reviewed manually
Recommendations
- Use confidence threshold (e.g., 0.7) for auto vs manual routing
- Periodically retrain with new data
- Monitor class-wise performance
How to Use
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "shkumar0511/insurance-lob-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1).numpy()[0]
return probs.argmax(), probs.max()
- Downloads last month
- 3