Instructions to use nates/LER-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nates/LER-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nates/LER-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nates/LER-roberta") model = AutoModelForTokenClassification.from_pretrained("nates/LER-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5a1ea86f63e537c2821e95315608528a440a5bdc29d75295aa1019d76098b75
- Size of remote file:
- 496 MB
- SHA256:
- 645ba5a76a0d7368e80a3f9a3fb036575d6ad18f6985037fdc50fc4bb838795e
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