Instructions to use KarthionLupenix/sap-ticket-classifier-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KarthionLupenix/sap-ticket-classifier-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KarthionLupenix/sap-ticket-classifier-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KarthionLupenix/sap-ticket-classifier-bert") model = AutoModelForSequenceClassification.from_pretrained("KarthionLupenix/sap-ticket-classifier-bert") - Notebooks
- Google Colab
- Kaggle
SAP Support Ticket Classifier
Fine-tuned bert-base-uncased that classifies SAP enterprise support tickets
into the correct SAP functional module.
Live Demo: https://karthionlupenix-sap-ticket-classifier-app.hf.space
Model ID: KarthionLupenix/sap-ticket-classifier-bert
Labels
| ID | Label | SAP Module |
|---|---|---|
| 0 | FI | Finance (GL, AP, AR, Asset Accounting, Bank) |
| 1 | MM | Materials Management (Purchasing, Inventory, MRP) |
| 2 | SD | Sales & Distribution (Orders, Delivery, Billing) |
| 3 | HR | Human Resources (Payroll, Time, Org Mgmt) |
Pipeline Overview
Usage
from transformers import pipeline
clf = pipeline("text-classification", model="KarthionLupenix/sap-ticket-classifier-bert")
result = clf("Vendor invoice not posted due to GR/IR account mismatch")
# [{'label': 'FI', 'score': 0.97}]
Or with all scores:
clf = pipeline(
"text-classification",
model="KarthionLupenix/sap-ticket-classifier-bert",
return_all_scores=True
)
clf("MRP not generating planned orders for raw material")
# [{'label': 'FI', 'score': 0.01}, {'label': 'MM', 'score': 0.96}, ...]
Training Details
- Base model:
bert-base-uncased - Architecture: BertForSequenceClassification (
[CLS]pooled โ Linear) - Max sequence length: 128
- Epochs: 4 (early stopping on weighted F1)
- Batch size: 16
- Learning rate: 2e-5 with linear warmup (10%)
- Optimizer: AdamW, weight decay 0.01
Intended Use
Route incoming SAP support tickets automatically to the correct functional team (Finance, MM, SD, HR) without manual triaging.
Limitations
- Trained on a small seed dataset โ production accuracy improves significantly with more labeled examples per class (500+ recommended).
- Covers 4 SAP modules; extend
LABELSinconfig.pyand retrain to add PP, PM, PS, QM, etc. - English only.
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