Text Classification
Transformers
Safetensors
xlm-roberta
ner
on-device
privacy
flowx
openner
cross
de-identification
text-embeddings-inference
Instructions to use flowxai/privacyfilter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/privacyfilter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="flowxai/privacyfilter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/privacyfilter") model = AutoModelForSequenceClassification.from_pretrained("flowxai/privacyfilter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52431b200f5a8e941fabe01d7039ef29365ce1a06d1d7a7ab3a8139faf3694df
- Size of remote file:
- 17.1 MB
- SHA256:
- 21898d7902cb2e75d6437ce24bd352ccb84af4774b5bb40539c688dcb2338f85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.