Instructions to use TensorGreed/brewslm-sensitive-data-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TensorGreed/brewslm-sensitive-data-tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TensorGreed/brewslm-sensitive-data-tagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TensorGreed/brewslm-sensitive-data-tagger") model = AutoModelForTokenClassification.from_pretrained("TensorGreed/brewslm-sensitive-data-tagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
BrewSLM export 37: sensitive-data span tagger, run #143 (deberta-v3-base, 30 types)
d1ddca2 verified Download tokenizer_config.json from TensorGreed/brewslm-sensitive-data-tagger: direct link, hf CLI and curl.
- Browser
- Download file 481 Bytes
-
https://huggingface.co/TensorGreed/brewslm-sensitive-data-tagger/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://TensorGreed/brewslm-sensitive-data-tagger/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/TensorGreed/brewslm-sensitive-data-tagger/resolve/main/tokenizer_config.json
481 Bytes
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "extra_special_tokens": [ | |
| "[PAD]", | |
| "[CLS]", | |
| "[SEP]" | |
| ], | |
| "is_local": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "unk_id": 3, | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |