Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
logistics
de-identification
Instructions to use flowxai/hazmatguard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/hazmatguard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/hazmatguard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/hazmatguard") model = AutoModelForTokenClassification.from_pretrained("flowxai/hazmatguard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model.onnx from flowxai/hazmatguard: direct link, hf CLI and curl.
- Browser
- Download file 2.62 MB
-
https://huggingface.co/flowxai/hazmatguard/resolve/main/onnx/model.onnx
- Command line
-
hf download hf://flowxai/hazmatguard/onnx/model.onnx
-
curl -L -o model.onnx https://huggingface.co/flowxai/hazmatguard/resolve/main/onnx/model.onnx
2.62 MB
- Xet hash:
- 7e5dc5ea289db3922cad0efa5863a21a85028c941a5aa44c50049ccf8a9e6477
- Size of remote file:
- 2.62 MB
- SHA256:
- bc6a052584ea53f2b9a768324363361fe91ee262f6b28adb24eab79c88cc5b25
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