Instructions to use Ngadou/bert-sms-spam-dectector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ngadou/bert-sms-spam-dectector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ngadou/bert-sms-spam-dectector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ngadou/bert-sms-spam-dectector") model = AutoModelForSequenceClassification.from_pretrained("Ngadou/bert-sms-spam-dectector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 573526ff39447cbf4c685bfa5cacfae1610c5e5db17fbf2df932c807e528a1b3
- Size of remote file:
- 438 MB
- SHA256:
- daa2d5fd0607f5ce58e4019197f40b952ce3258bd98df1331055d7149f8bbf67
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