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Hari5115/hinglish-retail-intent-classifier

sourceHugging Facemitupdated 4mo agoView on Hugging Face

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Model Card

Hinglish Retail Intent Classifier

Fine-tuned version of google/muril-base-cased for classifying Indian ecommerce customer support messages (Hinglish) into 13 intent categories.

Performance

—Test accuracy: 97.6%
—Macro F1: 97.6%

Label	Precision	Recall	F1	Support
cancel_order	1.00	1.00	1.00	22
damaged_item	1.00	1.00	1.00	23
delivery_complaint	0.83	0.87	0.85	23
discount_query	1.00	1.00	1.00	23
exchange_product	1.00	1.00	1.00	21
other	1.00	1.00	1.00	19
payment_issue	1.00	1.00	1.00	23
product_availability	1.00	1.00	1.00	23
product_information	1.00	1.00	1.00	21
refund_status	1.00	1.00	1.00	23
return_request	1.00	1.00	1.00	23
track_order	0.95	0.82	0.88	22
wrong_item	0.91	1.00	0.95	21

Usage

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Hari5115/hinglish-retail-intent-classifier"
)

result = classifier("mera order kab aayega?")
print(result)
# [{'label': 'track_order', 'score': 0.96}]

Intents supported

Label	Description
`track_order`	Customer asking where their order is or when it arrives
`cancel_order`	Customer wants to cancel a placed order
`exchange_product`	Customer received wrong size/colour, wants a swap
`refund_status`	Customer asking about refund timeline or whether it was processed
`delivery_complaint`	Late delivery, not delivered, delivery agent issue
`damaged_item`	Product arrived broken, damaged, or defective
`wrong_item`	Completely wrong product was delivered
`return_request`	Customer wants to return a product
`payment_issue`	Money deducted but order not placed, double charge
`product_availability`	Asking if an item is in stock or available in a size/colour
`product_information`	Asking about product details, material, dimensions
`discount_query`	Promo code not working, coupon issues, asking for discount
`other`	Anything that does not fit the above

Training data

Trained on Hari5115/hinglish-retail-intent-dataset

Training config

—Base model: google/muril-base-cased
—Epochs: 5
—Learning rate: 2e-5
—Batch size: 16 (train), 32 (eval)
—Max sequence length: 128
—Best model selected by macro F1 on validation set

Limitations

—Synthetic data — may not capture all real-world linguistic variation
—Primarily Hindi-English mixing; does not cover Tamil-English, Telugu-English, or other Indian language combinations
—Skewed toward common intents; rare edge cases are underrepresented