Instructions to use cp500/PII_annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cp500/PII_annotator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cp500/PII_annotator")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cp500/PII_annotator") model = AutoModelForTokenClassification.from_pretrained("cp500/PII_annotator") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24b4b97d608c4e50c1a9126c27faa6b731b17024ceb5205c26ccfbe3058198f7
- Size of remote file:
- 4.09 kB
- SHA256:
- 748f0d3837d0681267cff4613b0de8de03a26be2160fcb325ac937789a974a8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.