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")# pip install -U transformers accelerate # 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
Commit History
-
Update README.md
315ac32 -
Upload 12 files
2b00fc5 -
Delete roberta-base-bne-iris-capitel-ner
73f2c09 -
Upload 12 files
6ab6369 -
Update README.md
18c4354 -
Update README.md
3602c15 -
Update README.md
This is a Roberta multilingual (Catalan & Spanish) anonimization model, for use with BSC's AnonymizationPipeline at: https://github.com/TeMU-BSC/AnonymizationPipeline. The anonymization pipeline is a library for performing sensitive data identification and ultimately anonymization of the detected data in Spanish and Catalan user generated plain text. This is not a standalone model and is meant to work within the pipeline. The model can detect the following entities: ORG, PER, and LOCca298dd -
initial commit
bad6ab9