Token Classification
Transformers
GGUF
French
English
mistral
privacy
anonymization
pii
legal
compliance
gdpr
rgpd
ner
on-premise
sovereign-ai
slm
privamesh
Instructions to use sallani/PrivaMesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sallani/PrivaMesh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sallani/PrivaMesh")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sallani/PrivaMesh") model = AutoModelForTokenClassification.from_pretrained("sallani/PrivaMesh", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- d34fe2d3220ff96baefc46c896509483583128e49aff59406bc38c1f19a42e9d
- Size of remote file:
- 1.37 MB
- SHA256:
- 9418854829ddc5b3a0b6b7c5dae4142c0b02e9f6cbbc3cdaef194ab42b8694b3
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