Instructions to use DevQuasar/roberta-prompt_classifier-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevQuasar/roberta-prompt_classifier-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DevQuasar/roberta-prompt_classifier-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DevQuasar/roberta-prompt_classifier-v0.1") model = AutoModelForSequenceClassification.from_pretrained("DevQuasar/roberta-prompt_classifier-v0.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbe2e08f5df52ae26f4688be9c7b30daa903aafc8da4929d928a0eaa900f4d7d
- Size of remote file:
- 5.11 kB
- SHA256:
- a55d2a8f2e18764e3395bf3256e08dcd60d64d11df066d075a4a9bfa007adfb8
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