MsgSense SMS Classifier

This model predicts a composite label in the format:

<score>_<sms_type_id>

score (first digit): message importance used by app policy.
sms_type_id (second part): category id from SmsClassificationTypeEntity mapping.

Input Format

Sender: <clean_address> | Message: <normalized body>

Output Decoding

Example output label: 3_37

score = 3
sms_type_id = 37 (SERVICE_NOTIFICATION)

Usage

from transformers import pipeline

pipe = pipeline(
    "text-classification",
    model="imShub10/msgsense-sms-bert-base-cleanaddr-fulldata-20260424",
)

text = "Sender: DAIKIN | Message: Your service request is scheduled."
print(pipe(text))

BERT-base

accuracy: 0.95339
f1 (weighted): 0.95367
f1_macro: 0.89484
precision: 0.95624
recall: 0.95339
test_loss: 0.45452
train_loss: 0.10400
training_time_min: 70.2
model_size_mb: 418.5

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for imShub10/msgsense-sms-bert-base-cleanaddr-fulldata-20260424

Base model

google-bert/bert-base-uncased

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