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
metadata
language:
- en
pipeline_tag: token-classification
tags:
- medical
Protected health information (PHI) anonymization tool. Fine-tuned on the i2b2 2014 training dataset 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.