Instructions to use svassileva/multiclin_xlm_roberta_nl_procedure_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svassileva/multiclin_xlm_roberta_nl_procedure_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="svassileva/multiclin_xlm_roberta_nl_procedure_final")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("svassileva/multiclin_xlm_roberta_nl_procedure_final") model = AutoModelForTokenClassification.from_pretrained("svassileva/multiclin_xlm_roberta_nl_procedure_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 263c0a5cc9a933e330e77530d596683892537fe1377e61d7d9be27d8f468d048
- Size of remote file:
- 16.8 MB
- SHA256:
- afa602b67b7d4f3188dbb004e3daf4a5dc676612bfe7ea362a7f46b44e050d87
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