Instructions to use Werea-co/Werea-KVKK-PII-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Werea-co/Werea-KVKK-PII-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Werea-co/Werea-KVKK-PII-150M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Werea-co/Werea-KVKK-PII-150M") model = AutoModelForTokenClassification.from_pretrained("Werea-co/Werea-KVKK-PII-150M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d0fa9b2e18cfc8f38b6df38f7e1062c0425d746f899dc4b970bc65e781275f8
- Size of remote file:
- 5.91 kB
- SHA256:
- 046c08c88da15117ffe0dd6c8b84c30d5012146cd51e61b1dcadba76619cb6da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.