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:
- 22ca326ef66f65c16ebd6475aa5adc81591493e45af4212d83206c34b4d4d5ac
- Size of remote file:
- 1.11 GB
- SHA256:
- b91bd0c60fb6cbaf92ee9d6bb73496fed9ecf8ae601e55d6dae333594f194555
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