Text Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
healthcare
de-identification
text-embeddings-inference
Instructions to use flowxai/intentrouter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/intentrouter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="flowxai/intentrouter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/intentrouter") model = AutoModelForSequenceClassification.from_pretrained("flowxai/intentrouter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2724a6b7a97717a1d8e711ee83d3ef98bee3efef0d512212a9948c1a95b30de
- Size of remote file:
- 598 MB
- SHA256:
- 29e8d2e35db7fcb7da4de2518756b726bceb0a1a5403ae202f41af1663f81ac5
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