Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
logistics
de-identification
Instructions to use flowxai/portroute with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/portroute with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/portroute")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/portroute") model = AutoModelForTokenClassification.from_pretrained("flowxai/portroute", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from flowxai/portroute: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/flowxai/portroute/resolve/main/model.safetensors
- Command line
-
hf download hf://flowxai/portroute/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/flowxai/portroute/resolve/main/model.safetensors
598 MB
- Xet hash:
- c90089e8d2d8cdd8ef0c1a3c5592fdc2995df68e7e71e08d2b9d882a395c09bd
- Size of remote file:
- 598 MB
- SHA256:
- 004eaea87ae39e2f239bd14c3ebb47e89f2f905e2a1b85365522bded8325fb75
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