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:
- 39d6f237d6c7e3b5784996b4e1c1b31e371e111ccebcf09701f2a16df911fa30
- Size of remote file:
- 6.19 GB
- SHA256:
- c54a1a7e9b2f1313d0e61b81166a1319927935dfa8476e6ccca839903c81497e
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