Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
banking
de-identification
Instructions to use flowxai/filingtag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/filingtag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/filingtag")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/filingtag") model = AutoModelForTokenClassification.from_pretrained("flowxai/filingtag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb6a5fc2ec1cd49069a0969b6b7e544342e376fd9889a72724dce772a6bd5bd4
- Size of remote file:
- 599 MB
- SHA256:
- c7a356e34ed0be7a04500c16508ec8ac4899f0e05b85e2d244546c9158fe34cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.