Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
banking
de-identification
Instructions to use flowxai/amlnarrate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/amlnarrate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/amlnarrate")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/amlnarrate") model = AutoModelForTokenClassification.from_pretrained("flowxai/amlnarrate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30fbea8a40c875c9711eeec501814ec1da01758f22e4983ce210748906b852ba
- Size of remote file:
- 5.2 kB
- SHA256:
- 73540775a4480fddb1a3257a108da1284d74747270ae688b5683ba0b8b0df832
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