Instructions to use NimaKL/spamd_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NimaKL/spamd_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NimaKL/spamd_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NimaKL/spamd_model") model = AutoModelForSequenceClassification.from_pretrained("NimaKL/spamd_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa9bb6e47d6d5b386ed8ade20802e6e3262abed9da88dab43ae6b5c871f24efd
- Size of remote file:
- 443 MB
- SHA256:
- 9839050aa0a03a45eae17197a59d359742464a4cedbabd13897d58560dff2423
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