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:
- 03d60394ace5f8cccb84cf656147328e1069d012b098e06b6e59126a1334af0f
- Size of remote file:
- 496 MB
- SHA256:
- 2322a56610f6b0151e9380411b7ca1b8c5883e983a6dc1e4ecb1adf2cfcd6a66
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