Instructions to use jeongyoonhuh/bert-base-spam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeongyoonhuh/bert-base-spam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jeongyoonhuh/bert-base-spam")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jeongyoonhuh/bert-base-spam") model = AutoModelForSequenceClassification.from_pretrained("jeongyoonhuh/bert-base-spam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2bffd26bf62f91af5698e37847ba280b5f9080f0dc27d1d7798a490feef686f2
- Size of remote file:
- 438 MB
- SHA256:
- c2c95ac58a4be431ee8472067b01deb636e5c611bbdd0e2115de2e82e52ce15f
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