Instructions to use breadlicker45/rlhf-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/rlhf-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="breadlicker45/rlhf-class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("breadlicker45/rlhf-class") model = AutoModelForSequenceClassification.from_pretrained("breadlicker45/rlhf-class") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0554bffed18aa764c120982b6ab5f815c2fbf4b1b25fd247f477100749f5d53d
- Size of remote file:
- 268 MB
- SHA256:
- d17f8663a1482e9067ee2cab0ceb711fa42efecbb7a8f39c0468b56a8669993d
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