Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
logistics
de-identification
Instructions to use flowxai/containerdetect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/containerdetect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/containerdetect")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/containerdetect") model = AutoModelForTokenClassification.from_pretrained("flowxai/containerdetect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7280a0984099ec4b0ddb08e0d1e7d7ea961f876f3b28b360c826695166f484a9
- Size of remote file:
- 598 MB
- SHA256:
- 0b755348e4d0d512a1cc2e45bc7e34a09addf37051cdedf81dac51ac649f285e
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