Text Classification
Transformers
ONNX
Safetensors
bert
scam-detection
fraud-detection
indic
nlp
text-embeddings-inference
Instructions to use anmolshrivastav/indicbert-scam-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anmolshrivastav/indicbert-scam-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anmolshrivastav/indicbert-scam-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anmolshrivastav/indicbert-scam-classifier") model = AutoModelForSequenceClassification.from_pretrained("anmolshrivastav/indicbert-scam-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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http://bit.ly/punjab-visa
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example_title: Punjabi Scam
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library_name: transformers
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# IndicBERT Multilingual Scam & Fraud Classifier
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http://bit.ly/punjab-visa
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example_title: Punjabi Scam
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library_name: transformers
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datasets:
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- anmolshrivastav/scam_ham_india_14_languages
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base_model:
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- ai4bharat/IndicBERTv2-MLM-only
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# IndicBERT Multilingual Scam & Fraud Classifier
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