Instructions to use bthomas279/scam-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bthomas279/scam-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bthomas279/scam-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bthomas279/scam-detector") model = AutoModelForSequenceClassification.from_pretrained("bthomas279/scam-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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datasets:
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- ucirvine/sms_spam
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- ealvaradob/phishing-dataset
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language:
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- en
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base_model:
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Known Biases (Positive leaning(scam/spam):
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- Promotions in general
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- Texts mentioning money
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- Advertising
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datasets:
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- ucirvine/sms_spam
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- ealvaradob/phishing-dataset
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- Deysi/spam-detection-dataset
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- webis/tldr-17
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- KaiNylund/twitter-year-splits
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language:
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- en
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base_model:
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Known Biases (Positive leaning(scam/spam):
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- Promotions in general
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- Texts mentioning money
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- Advertising
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