Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
logistics
de-identification
Instructions to use flowxai/traderedact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/traderedact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/traderedact")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/traderedact") model = AutoModelForTokenClassification.from_pretrained("flowxai/traderedact", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf7f0054c1ec9c407026dca26eddf4d8754276e828a2a0d3edb93bd8e59601b4
- Size of remote file:
- 5.2 kB
- SHA256:
- d987a59e9c612b37789ce48a6b30dda2181713f4499728a8d6f93bbf29d8bb3c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.