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:
- 34cc0a0249372df905a7525358931e6e4c6ca3e5c26358dfe8ca2aa6a6328ba6
- Size of remote file:
- 598 MB
- SHA256:
- e5cbba42ddae17db2c8130dd09430a1a8bfe4251cded66369cd13af94a3ff53b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.