Token Classification
Transformers
Safetensors
English
Spanish
qwen3
pii
privacy
named-entity-recognition
text-generation-inference
Instructions to use valendra/privacy-filter-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use valendra/privacy-filter-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="valendra/privacy-filter-mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("valendra/privacy-filter-mini") model = AutoModelForTokenClassification.from_pretrained("valendra/privacy-filter-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from valendra/privacy-filter-mini: direct link, hf CLI and curl.
- Browser
- Download file 3.46 kB
-
https://huggingface.co/valendra/privacy-filter-mini/resolve/main/config.json
- Command line
-
hf download hf://valendra/privacy-filter-mini/config.json
-
curl -L -o config.json https://huggingface.co/valendra/privacy-filter-mini/resolve/main/config.json
3.46 kB
| { | |
| "architectures": [ | |
| "Qwen3ForTokenClassification" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 151643, | |
| "eos_token_id": 151643, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-PERSON", | |
| "2": "I-PERSON", | |
| "3": "E-PERSON", | |
| "4": "S-PERSON", | |
| "5": "B-EMAIL", | |
| "6": "I-EMAIL", | |
| "7": "E-EMAIL", | |
| "8": "S-EMAIL", | |
| "9": "B-PHONE", | |
| "10": "I-PHONE", | |
| "11": "E-PHONE", | |
| "12": "S-PHONE", | |
| "13": "B-ADDRESS", | |
| "14": "I-ADDRESS", | |
| "15": "E-ADDRESS", | |
| "16": "S-ADDRESS", | |
| "17": "B-GOVERNMENT_ID", | |
| "18": "I-GOVERNMENT_ID", | |
| "19": "E-GOVERNMENT_ID", | |
| "20": "S-GOVERNMENT_ID", | |
| "21": "B-FINANCIAL_ID", | |
| "22": "I-FINANCIAL_ID", | |
| "23": "E-FINANCIAL_ID", | |
| "24": "S-FINANCIAL_ID", | |
| "25": "B-SECRET", | |
| "26": "I-SECRET", | |
| "27": "E-SECRET", | |
| "28": "S-SECRET", | |
| "29": "B-IP_ADDRESS", | |
| "30": "I-IP_ADDRESS", | |
| "31": "E-IP_ADDRESS", | |
| "32": "S-IP_ADDRESS", | |
| "33": "B-DATE_OF_BIRTH", | |
| "34": "I-DATE_OF_BIRTH", | |
| "35": "E-DATE_OF_BIRTH", | |
| "36": "S-DATE_OF_BIRTH", | |
| "37": "B-URL", | |
| "38": "I-URL", | |
| "39": "E-URL", | |
| "40": "S-URL" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-ADDRESS": 13, | |
| "B-DATE_OF_BIRTH": 33, | |
| "B-EMAIL": 5, | |
| "B-FINANCIAL_ID": 21, | |
| "B-GOVERNMENT_ID": 17, | |
| "B-IP_ADDRESS": 29, | |
| "B-PERSON": 1, | |
| "B-PHONE": 9, | |
| "B-SECRET": 25, | |
| "B-URL": 37, | |
| "E-ADDRESS": 15, | |
| "E-DATE_OF_BIRTH": 35, | |
| "E-EMAIL": 7, | |
| "E-FINANCIAL_ID": 23, | |
| "E-GOVERNMENT_ID": 19, | |
| "E-IP_ADDRESS": 31, | |
| "E-PERSON": 3, | |
| "E-PHONE": 11, | |
| "E-SECRET": 27, | |
| "E-URL": 39, | |
| "I-ADDRESS": 14, | |
| "I-DATE_OF_BIRTH": 34, | |
| "I-EMAIL": 6, | |
| "I-FINANCIAL_ID": 22, | |
| "I-GOVERNMENT_ID": 18, | |
| "I-IP_ADDRESS": 30, | |
| "I-PERSON": 2, | |
| "I-PHONE": 10, | |
| "I-SECRET": 26, | |
| "I-URL": 38, | |
| "O": 0, | |
| "S-ADDRESS": 16, | |
| "S-DATE_OF_BIRTH": 36, | |
| "S-EMAIL": 8, | |
| "S-FINANCIAL_ID": 24, | |
| "S-GOVERNMENT_ID": 20, | |
| "S-IP_ADDRESS": 32, | |
| "S-PERSON": 4, | |
| "S-PHONE": 12, | |
| "S-SECRET": 28, | |
| "S-URL": 40 | |
| }, | |
| "layer_types": [ | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 32768, | |
| "max_window_layers": 28, | |
| "model_type": "qwen3", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 8, | |
| "privacy_attention_mode": "causal", | |
| "privacy_initialization_weights_sha256": "1f989d08382dce6598f94a307ba6a8ec23fb1f0b103bf55408b1b1d4c4fddc64", | |
| "privacy_minimum_stopping_epoch": 1.0, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000, | |
| "sliding_window": null, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.55.4", | |
| "use_cache": false, | |
| "use_sliding_window": false, | |
| "vocab_size": 151936 | |
| } | |