Instructions to use julian-schelb/roberta-ner-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use julian-schelb/roberta-ner-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="julian-schelb/roberta-ner-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("julian-schelb/roberta-ner-multilingual") model = AutoModelForTokenClassification.from_pretrained("julian-schelb/roberta-ner-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3794c815b74550e6cb094eaa573d50040b42db6891341ee48ad301dd4bcb830d
- Size of remote file:
- 2.24 GB
- SHA256:
- 0b7ce6892f1b6d6ef6d5c24bab4f2d522316c65cc9de9e51faae0327a983e939
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