Instructions to use Tirendaz/multilingual-xlm-roberta-for-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tirendaz/multilingual-xlm-roberta-for-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Tirendaz/multilingual-xlm-roberta-for-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Tirendaz/multilingual-xlm-roberta-for-ner") model = AutoModelForTokenClassification.from_pretrained("Tirendaz/multilingual-xlm-roberta-for-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc9cdab670ce1938db084c3303df9b0a8a317a99f60729ff0c91f84dc2250b61
- Size of remote file:
- 1.11 GB
- SHA256:
- 64401e7a589c2dfd1eaf006aa80d949acea2a4dc650cd7468be470c7bbea9dc9
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