Token Classification
Transformers
PyTorch
English
bert
sequence-tagger-model
pubmedbert
uncased
radiology
biomedical
bdf-toolbox
Instructions to use StanfordAIMI/stanford-deidentifier-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordAIMI/stanford-deidentifier-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="StanfordAIMI/stanford-deidentifier-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/stanford-deidentifier-base") model = AutoModel.from_pretrained("StanfordAIMI/stanford-deidentifier-base") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#7 opened about 1 month ago
by
vigneshwar234
Adding `safetensors` variant of this model
#5 opened over 1 year ago
by
SFconvertbot
Which Training Datasets Were Used?
👍 8
1
#4 opened over 2 years ago
by
comadan
Adding `safetensors` variant of this model
#2 opened over 3 years ago
by
SFconvertbot
Add TF weights
1
#1 opened over 3 years ago
by
Rocketknight1