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:
- 5f3c44983f1cc32be357603afe9ac84afbb4a3481c2833eef55ea093fef59832
- Size of remote file:
- 993 MB
- SHA256:
- aea2e481cfee2e9dc1637f1248b2d24f43748332fc25ee87742f06d0fcd3aaf6
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