Token Classification
Transformers
Safetensors
Russian
English
bert
ner
pii
secret-detection
credentials
masking
russian
Instructions to use fef2/ner_rus_bert-secret_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fef2/ner_rus_bert-secret_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fef2/ner_rus_bert-secret_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fef2/ner_rus_bert-secret_detection") model = AutoModelForTokenClassification.from_pretrained("fef2/ner_rus_bert-secret_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-PERSON", | |
| "2": "I-PERSON", | |
| "3": "B-LOCATION", | |
| "4": "I-LOCATION", | |
| "5": "B-ORGANIZATION", | |
| "6": "I-ORGANIZATION", | |
| "7": "B-LOGIN", | |
| "8": "I-LOGIN", | |
| "9": "B-PASSWORD", | |
| "10": "I-PASSWORD", | |
| "11": "B-AUTH_TOKEN", | |
| "12": "I-AUTH_TOKEN", | |
| "13": "B-SECRET_KEY", | |
| "14": "I-SECRET_KEY", | |
| "15": "B-CONTRACT_NUMBER", | |
| "16": "I-CONTRACT_NUMBER" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-AUTH_TOKEN": 11, | |
| "B-CONTRACT_NUMBER": 15, | |
| "B-LOCATION": 3, | |
| "B-LOGIN": 7, | |
| "B-ORGANIZATION": 5, | |
| "B-PASSWORD": 9, | |
| "B-PERSON": 1, | |
| "B-SECRET_KEY": 13, | |
| "I-AUTH_TOKEN": 12, | |
| "I-CONTRACT_NUMBER": 16, | |
| "I-LOCATION": 4, | |
| "I-LOGIN": 8, | |
| "I-ORGANIZATION": 6, | |
| "I-PASSWORD": 10, | |
| "I-PERSON": 2, | |
| "I-SECRET_KEY": 14, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 119547 | |
| } | |