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
File size: 1,736 Bytes
3c924ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | {
"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
}
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