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:
- 512be7c8a878f220384c15c4586a17ebbbabc34ec104a79023628d77b14867b3
- Size of remote file:
- 3.12 kB
- SHA256:
- dde37f84323c61f149d5d8177e1b6a413f7d8dc34f34ddb2909f69e7a23cc9a3
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