Token Classification
Transformers
Safetensors
deberta-v2
pii
pii-detection
pii-masking
redaction
privacy
deberta-v3
Eval Results (legacy)
Instructions to use amsintelligence/masker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amsintelligence/masker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="amsintelligence/masker")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("amsintelligence/masker") model = AutoModelForTokenClassification.from_pretrained("amsintelligence/masker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,098 Bytes
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"architectures": [
"DebertaV2ForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"dtype": "bfloat16",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "B-AGE",
"2": "I-AGE",
"3": "E-AGE",
"4": "S-AGE",
"5": "B-BUILDING_NUMBER",
"6": "I-BUILDING_NUMBER",
"7": "E-BUILDING_NUMBER",
"8": "S-BUILDING_NUMBER",
"9": "B-CITY",
"10": "I-CITY",
"11": "E-CITY",
"12": "S-CITY",
"13": "B-CREDIT_CARD",
"14": "I-CREDIT_CARD",
"15": "E-CREDIT_CARD",
"16": "S-CREDIT_CARD",
"17": "B-DATE",
"18": "I-DATE",
"19": "E-DATE",
"20": "S-DATE",
"21": "B-EMAIL",
"22": "I-EMAIL",
"23": "E-EMAIL",
"24": "S-EMAIL",
"25": "B-GIVEN_NAME",
"26": "I-GIVEN_NAME",
"27": "E-GIVEN_NAME",
"28": "S-GIVEN_NAME",
"29": "B-GOVERNMENT_ID",
"30": "I-GOVERNMENT_ID",
"31": "E-GOVERNMENT_ID",
"32": "S-GOVERNMENT_ID",
"33": "B-PHONE",
"34": "I-PHONE",
"35": "E-PHONE",
"36": "S-PHONE",
"37": "B-STREET_NAME",
"38": "I-STREET_NAME",
"39": "E-STREET_NAME",
"40": "S-STREET_NAME",
"41": "B-SURNAME",
"42": "I-SURNAME",
"43": "E-SURNAME",
"44": "S-SURNAME",
"45": "B-ZIP_CODE",
"46": "I-ZIP_CODE",
"47": "E-ZIP_CODE",
"48": "S-ZIP_CODE"
},
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"intermediate_size": 3072,
"label2id": {
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"E-STREET_NAME": 39,
"E-SURNAME": 43,
"E-ZIP_CODE": 47,
"I-AGE": 2,
"I-BUILDING_NUMBER": 6,
"I-CITY": 10,
"I-CREDIT_CARD": 14,
"I-DATE": 18,
"I-EMAIL": 22,
"I-GIVEN_NAME": 26,
"I-GOVERNMENT_ID": 30,
"I-PHONE": 34,
"I-STREET_NAME": 38,
"I-SURNAME": 42,
"I-ZIP_CODE": 46,
"O": 0,
"S-AGE": 4,
"S-BUILDING_NUMBER": 8,
"S-CITY": 12,
"S-CREDIT_CARD": 16,
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"S-GIVEN_NAME": 28,
"S-GOVERNMENT_ID": 32,
"S-PHONE": 36,
"S-STREET_NAME": 40,
"S-SURNAME": 44,
"S-ZIP_CODE": 48
},
"layer_norm_eps": 1e-07,
"legacy": true,
"max_position_embeddings": 512,
"max_relative_positions": -1,
"model_type": "deberta-v2",
"norm_rel_ebd": "layer_norm",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"pooler_dropout": 0,
"pooler_hidden_act": "gelu",
"pooler_hidden_size": 768,
"pos_att_type": [
"p2c",
"c2p"
],
"position_biased_input": false,
"position_buckets": 256,
"relative_attention": true,
"share_att_key": true,
"transformers_version": "4.57.6",
"type_vocab_size": 0,
"vocab_size": 136671
}
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