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:
- 8d1a25b0b55bef3494d6ad8df7f2ac7560efb10c4781ab88900819c255cb88dd
- Size of remote file:
- 504 MB
- SHA256:
- 04ae188c05891e9f33b4a18a419f81445ca849e7af3f0945e748833673c2aeba
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