Instructions to use udit-k/HamSpamBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use udit-k/HamSpamBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="udit-k/HamSpamBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("udit-k/HamSpamBERT") model = AutoModelForSequenceClassification.from_pretrained("udit-k/HamSpamBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cb28cf08e0420b70a22f0d3e44dbbbaadbfcde7a9752f268461571007f5041a
- Size of remote file:
- 438 MB
- SHA256:
- 973471076938e9d6c35bc763184f6fb975d2a86a147d54216a8aeb46aeb22791
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