Token Classification
PEFT
Safetensors
Transformers
Turkish
lora
pii-detection
privacy
kvkk
turkish
named-entity-recognition
Instructions to use negentropi/belgin-privacy-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use negentropi/belgin-privacy-filter with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("openai/privacy-filter") model = PeftModel.from_pretrained(base_model, "negentropi/belgin-privacy-filter") - Transformers
How to use negentropi/belgin-privacy-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="negentropi/belgin-privacy-filter")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("negentropi/belgin-privacy-filter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from negentropi/belgin-privacy-filter: direct link, hf CLI and curl.
- Browser
- Download file 284 Bytes
-
https://huggingface.co/negentropi/belgin-privacy-filter/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://negentropi/belgin-privacy-filter/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/negentropi/belgin-privacy-filter/resolve/main/tokenizer_config.json
284 Bytes
| { | |
| "backend": "tokenizers", | |
| "eos_token": "<|endoftext|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 128000, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |