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:
- 05e207551ff4f7ea125b22cbd3dae598ec35cdfd0da5d28f56efb126fa319dab
- Size of remote file:
- 4.34 kB
- SHA256:
- ea004d17f42bf5e202a47c65ccbb70b14d7f37948f7fb2e03513dff21d29b82f
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