Instructions to use ddxdaniel/rm-ld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ddxdaniel/rm-ld with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ddxdaniel/rm-ld")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ddxdaniel/rm-ld") model = AutoModelForSequenceClassification.from_pretrained("ddxdaniel/rm-ld", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c61884cb2d49cc6202af076e6df4cf139a212cac1295c03f9e6672225a66e08
- Size of remote file:
- 3.1 GB
- SHA256:
- ae581880dd36b69d5f887b47e6a82a3a45d47a1f8c3f4b804f4c1da1ee1d89df
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