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")# pip install -U transformers accelerate # 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
Download tokenizer_config.json from flowxai/privacyfilter: direct link, hf CLI and curl.
- Browser
- Download file 343 Bytes
-
https://huggingface.co/flowxai/privacyfilter/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://flowxai/privacyfilter/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/flowxai/privacyfilter/resolve/main/tokenizer_config.json
343 Bytes
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |