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")# 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
- Xet hash:
- 2c00243139c9713b6a82fcd82cca53bd1a442534c9b1d1dcf9edc835c212b613
- Size of remote file:
- 5.2 kB
- SHA256:
- eac23f2d332ce0dccc0ac993e6d0f58f748cf9a6f10f8323fc2650cc137ac536
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.