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:
- e7a7eef97291c8846944b251dbdf1c67bad8069f062129278886179781715b56
- Size of remote file:
- 4.09 kB
- SHA256:
- 9418862752f4b58786017dd945b3031896eb0d50f0b350f7aa640c34671c60f2
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