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")# pip install -U transformers accelerate # 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
Commit History
30 days ago
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Remove a false checksum guarantee, say what the 1.0000 F1 is, state the training domain
Verified signatureThe card claimed identifiers are validated by checksum, as 'a correctness guarantee general LLMs lack'. No repository in the OpenNER family ships validation code, and a ModernBERT encoder cannot run a check digit, so the claim was false for every model in the family. The held-out F1 is now explained rather than headlined: train and test come from one generator, and two models from the same pipeline (filingtag 0.6444, ibandetect 0.9488) show it can report below 1.0, so a 1.0 means the generated task was trivially separable. Also removes a pointer to an OpenNER benchmark that is not published, and adds a 'Trained on' line naming the actual domain.d3a4797
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Add OpenNER model, card, NOTICE (Apache-2.0, FlowX.AI)
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initial commit
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