Instructions to use faridlazuarda/data_laundry with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faridlazuarda/data_laundry with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faridlazuarda/data_laundry")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("faridlazuarda/data_laundry") model = AutoModelForSequenceClassification.from_pretrained("faridlazuarda/data_laundry", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e55b789671da6c1739ff566c0ecfe32754af156c9dae4a2127e751440d142103
- Size of remote file:
- 3.96 kB
- SHA256:
- d0980f24265a5414d03b422f9bceb3fd6a12bba7b58f898534ee3151cfbfc13d
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