Instructions to use KindLab/roberta-deid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KindLab/roberta-deid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KindLab/roberta-deid")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KindLab/roberta-deid") model = AutoModelForTokenClassification.from_pretrained("KindLab/roberta-deid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from KindLab/roberta-deid: direct link, hf CLI and curl.
- Browser
- Download file 561 Bytes
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https://huggingface.co/KindLab/roberta-deid/resolve/main/README.md
- Command line
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hf download hf://KindLab/roberta-deid/README.md
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curl -L -o README.md https://huggingface.co/KindLab/roberta-deid/resolve/main/README.md
561 Bytes
| language: | |
| - en | |
| pipeline_tag: token-classification | |
| tags: | |
| - medical | |
| Protected health information (PHI) anonymization tool. Fine-tuned on the [i2b2 2014 training dataset](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989908/) from the pretrained `roberta-base` model. | |
| Anonymizes according to the i2b2 2014 standard, including all ages, locations and organizations, dates (including lone years), names, professions, identification numbers, and contact information. | |
| Model released with the approval of Informatics for Integrating Biology & the Bedside. |