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")# pip install -U transformers accelerate # 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
Commit History
-
Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (7931e1af15dc55b7b53c0c41773a67a70cdd4480) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>a52c572
-
Update config.json
5a88808 -
Update config.json
90424c4
-
Upload tokenizer
ef6a646 -
Update README.md
68f1a02 -
Upload BertForSequenceClassification
7a8acee -
Upload BertForSequenceClassification
91ad746 -
initial commit
05ef6be