Token Classification
Transformers
Safetensors
English
Hindi
xlm-roberta
pii
ner
asr
redaction
privacy
lexguard
multilingual
huggingface
Instructions to use sanskxr02/zentrypii-278m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanskxr02/zentrypii-278m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sanskxr02/zentrypii-278m")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sanskxr02/zentrypii-278m") model = AutoModelForTokenClassification.from_pretrained("sanskxr02/zentrypii-278m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from sanskxr02/zentrypii-278m: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/sanskxr02/zentrypii-278m/resolve/main/model.safetensors
- Command line
-
hf download hf://sanskxr02/zentrypii-278m/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/sanskxr02/zentrypii-278m/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- 819e9a932b94612ba51a3e054eb9dcf6c48115028e1714b44aa12a64f6fdcc3b
- Size of remote file:
- 1.11 GB
- SHA256:
- d974a7d966c04db0521633089777b3a76567294d7a9871a15a18607e9de32f69
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