Token Classification
Transformers
Safetensors
English
Chinese
xlm-roberta
pii
pii-detection
de-identification
anonymization
redaction
legal
ner
Instructions to use libraos/veil-detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use libraos/veil-detect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="libraos/veil-detect")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("libraos/veil-detect") model = AutoModelForTokenClassification.from_pretrained("libraos/veil-detect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from libraos/veil-detect: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/libraos/veil-detect/resolve/main/config.json
- Command line
-
hf download hf://libraos/veil-detect/config.json
-
curl -L -o config.json https://huggingface.co/libraos/veil-detect/resolve/main/config.json
1.74 kB
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "XLMRobertaForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-person", | |
| "2": "I-person", | |
| "3": "B-organization", | |
| "4": "I-organization", | |
| "5": "B-location", | |
| "6": "I-location", | |
| "7": "B-contact", | |
| "8": "I-contact", | |
| "9": "B-id", | |
| "10": "I-id", | |
| "11": "B-financial", | |
| "12": "I-financial", | |
| "13": "B-credential", | |
| "14": "I-credential", | |
| "15": "B-date", | |
| "16": "I-date", | |
| "17": "B-demographic", | |
| "18": "I-demographic", | |
| "19": "B-date_of_birth", | |
| "20": "I-date_of_birth" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "B-contact": 7, | |
| "B-credential": 13, | |
| "B-date": 15, | |
| "B-date_of_birth": 19, | |
| "B-demographic": 17, | |
| "B-financial": 11, | |
| "B-id": 9, | |
| "B-location": 5, | |
| "B-organization": 3, | |
| "B-person": 1, | |
| "I-contact": 8, | |
| "I-credential": 14, | |
| "I-date": 16, | |
| "I-date_of_birth": 20, | |
| "I-demographic": 18, | |
| "I-financial": 12, | |
| "I-id": 10, | |
| "I-location": 6, | |
| "I-organization": 4, | |
| "I-person": 2, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.12.1", | |
| "type_vocab_size": 1, | |
| "use_cache": false, | |
| "vocab_size": 250002 | |
| } | |