Text Classification
Transformers
LiteRT
ONNX
Safetensors
English
distilbert
sms
sms-classification
clean-address
msgsense
text-embeddings-inference
Instructions to use imShub10/msgsense-sms-distilbert-cleanaddr-fulldata-20260424 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imShub10/msgsense-sms-distilbert-cleanaddr-fulldata-20260424 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="imShub10/msgsense-sms-distilbert-cleanaddr-fulldata-20260424")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("imShub10/msgsense-sms-distilbert-cleanaddr-fulldata-20260424") model = AutoModelForSequenceClassification.from_pretrained("imShub10/msgsense-sms-distilbert-cleanaddr-fulldata-20260424") - Notebooks
- Google Colab
- Kaggle
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 fromSmsClassificationTypeEntitymapping.
Input Format
Sender: <clean_address> | Message: <normalized body>
Output Decoding
Example output label: 3_37
score = 3sms_type_id = 37(SERVICE_NOTIFICATION)
Usage
from transformers import pipeline
pipe = pipeline(
"text-classification",
model="imShub10/msgsense-sms-distilbert-cleanaddr-fulldata-20260424",
)
text = "Sender: DAIKIN | Message: Your service request is scheduled."
print(pipe(text))
DistilBERT
- accuracy:
0.95188 - f1 (weighted):
0.95205 - f1_macro:
0.92125 - precision:
0.95353 - recall:
0.95188 - test_loss:
0.31780 - train_loss:
0.02321 - training_time_min:
34.0 - model_size_mb:
256.2
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Model tree for imShub10/msgsense-sms-distilbert-cleanaddr-fulldata-20260424
Base model
distilbert/distilbert-base-uncased