Instructions to use dwisaji/Modelroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwisaji/Modelroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dwisaji/Modelroberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dwisaji/Modelroberta") model = AutoModelForSequenceClassification.from_pretrained("dwisaji/Modelroberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc09d7640c87c7e3dbf4f1699a1f5c5df71a398f110bc5eb605539f9c11a1027
- Size of remote file:
- 3.38 kB
- SHA256:
- 7c44e42417e8af9d96fb592a37f6db2b5c2e217015e2277a282d99b6e7a1fecf
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