Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
cross
de-identification
Instructions to use flowxai/kybextract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/kybextract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/kybextract")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/kybextract") model = AutoModelForTokenClassification.from_pretrained("flowxai/kybextract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download metrics.json from flowxai/kybextract: direct link, hf CLI and curl.
- Browser
- Download file 243 Bytes
-
https://huggingface.co/flowxai/kybextract/resolve/main/metrics.json
- Command line
-
hf download hf://flowxai/kybextract/metrics.json
-
curl -L -o metrics.json https://huggingface.co/flowxai/kybextract/resolve/main/metrics.json
243 Bytes
| { | |
| "test_loss": 8.47950104798656e-07, | |
| "test_precision": 1.0, | |
| "test_recall": 1.0, | |
| "test_f1": 1.0, | |
| "test_accuracy": 1.0, | |
| "test_runtime": 4.8071, | |
| "test_samples_per_second": 832.095, | |
| "test_steps_per_second": 26.003, | |
| "epoch": 3.0 | |
| } |