Instructions to use BSC-LT/roberta_model_for_anonimization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/roberta_model_for_anonimization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BSC-LT/roberta_model_for_anonimization")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BSC-LT/roberta_model_for_anonimization") model = AutoModelForTokenClassification.from_pretrained("BSC-LT/roberta_model_for_anonimization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96f7b93c75d036ebe9915c1fd07df35e7ba90d07caaee74acf515204e6ef3047
- Size of remote file:
- 2.54 kB
- SHA256:
- 372826fd22322afeabb836f01b27229bb024f143adf407e671ef9698d56f638d
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